Integrative Oncology (IO) in France aligns with the definition proposed by the Society for Integrative Oncology, combining conventional cancer treatments with evidence-based complementary therapies within an interdisciplinary, patient-centered framework. Unlike alternative approaches, integrative therapies are used alongside standard oncological care. In France, a wide range of complementary therapies are implemented, with some-such as homeopathy and acupuncture-more widely integrated, reflecting historical use and favorable safety profiles.Over the past decade, IO has developed through diverse organizational models, including hospital-based day care programs, dedicated consultations in cancer centers, and community-based structures. These models provide access to personalized and coordinated care pathways, with no out-of-pocket cost for patients, supported by the national health insurance system or alternative funding sources. In 2023, the French Society for Integrative Oncology was established to structure and promote this approach nationwide and to foster connections with the European branch of the Society for Integrative Oncology.Compared with international models, the French approach appears distinctive in combining hospital-based and community-based structures, thereby facilitating broader accessibility while maintaining coordination with conventional care. This hybrid organization contrasts with predominantly academic models in the United States and more systematically integrated hospital-based systems in countries such as Israel.Available data and patient-reported outcomes suggest improvements in quality of life and treatment adherence. However, marked heterogeneity across programs and a predominantly observational evidence base highlight the need for controlled trials specifically designed to evaluate the particularities of integrative oncology.Overall, integrative oncology in France represents a pragmatic and evolving model that may contribute to reshaping supportive cancer care by combining accessibility, coordination, and patient-centered approaches.

Study DesignRetrospective cohort study of prospectively enrolled patients.ObjectivesTo evaluate the association between postoperative slip reduction and improvement in disability and low back pain (LBP) after lumbar fusion for low-grade degenerative spondylolisthesis (DLS), and to determine whether outcomes are independently associated with slip reduction or with sagittal alignment changes.Methods160 patients undergoing lumbar fusion for low-grade DLS were analyzed. Slip reduction was quantified on lateral radiographs as absolute change in relative slippage normalized to vertebral body dimensions. Disability and LBP were assessed using the Oswestry Disability Index (ODI) and numeric rating scale (NRS) preoperatively and at two years. Associations between slip reduction and improvement in ODI and LBP were analyzed using multivariable linear regression adjusted for demographics, baseline symptoms, fusion length, and preoperative slippage.ResultsSlip reduction was independently associated with greater improvement in ODI and LBP (both p<0.001) at two years. Although slip reduction correlated with changes in segmental slip angle, lumbar lordosis, and PI-LL (all p≤0.001), these alignment parameters were not independently associated with clinical outcomes (all p>0.05).ConclusionsPostoperative slip reduction was independently associated with greater improvement in disability and LBP among patients undergoing lumbar fusion with attempted reduction for low-grade DLS, whereas associated changes in sagittal alignment were not. However, given the moderate explanatory power of the regression models, slip reduction should be interpreted as one of several factors associated with postoperative outcomes. As this was an observational analysis, residual confounding by indication cannot be excluded, and prospective validation is warranted to further define the role of slip reduction in surgical decision-making.

Chitin nano- and mesoscale structures present in the exoskeleton of crustaceans exhibit exceptional longitudinal stiffness and toughness, rivaling or even exceeding that of many synthetic polymer architectures. Here, we reveal the origin of the asymmetric shear response in chitin multiscale architectures, marked by pronounced anisotropy in deformation. Under shear aligned with the molecular axis, chitin accommodates strain through coherent atomic rearrangements that enable elastic recovery. In contrast, shear applied perpendicular to this axis induces liquid-like plasticity via localized sliding. These results demonstrate the intrinsic mechanical anisotropy of chitin, underpinning a dual function in resisting repetitive loading while dissipating internal stress during high-strain events. Our findings establish the molecular basis of shear elastoplasticity in multiscale chitin structures, wherein axial elasticity supports energy storage in load-bearing regions, whereas transverse plasticity enables controlled energy dissipation. These atomic-scale insights lay a foundation for the predictive design of chitin-based materials with tunable strength-toughness profiles.

Study DesignMulticenter prospective cohort study.ObjectivesThis study aimed to determine whether concomitant cervical anterolisthesis influences surgical outcomes in patients with cervical spondylotic myelopathy (CSM).MethodsA total of 691 CSM patients were enrolled and classified into those with anterolisthesis ≥ 2 mm (Slip group, n=95) and those without measurable slip (Non-slip group, n=596). Patients underwent surgical treatment as indicated and were followed for 2 years. Cervical alignment, C2-7 ROM, JOA scores, JOACMEQ domains, and SF-36 scores were assessed. Clinical outcomes were compared using multivariate analyses adjusted for baseline factors. A subgroup analysis evaluated high-grade slip (≥4 mm).ResultsThe Slip group was older and had more kyphosis and worse baseline lordosis and JOA score. Fusion was performed more frequently in the Slip group, which also showed a greater reduction in cervical ROM at 2 years. Adjusted postoperative JOA score, JOACMEQ, and SF-36 outcomes were comparable between groups postoperatively. Within the Slip group, high-grade slip (≥4 mm) was associated with less JOA improvement in an exploratory subgroup analysis, whereas JOACMEQ and SF-36 scores were not influenced by slip severity.ConclusionsNeutral-radiograph-defined cervical anterolisthesis ≥2 mm was not independently associated with worse adjusted 2-year neurological, functional, or quality-of-life outcomes. High-grade slip (≥4 mm), however, may limit neurological recovery, underscoring the importance of evaluating segmental stability when substantial slippage is present.

Study DesignCross-sectional survey.ObjectiveOutcomes research has significantly shaped patient management in spine deformity care, while the dissemination of research remains a challenge. To guide future research directions, this study surveyed AO Spine members to identify the research needs, dissemination methods, and existing knowledge gaps.MethodsAn anonymous, English-language survey containing 40 questions explored knowledge gaps, research priorities, and knowledge translation practices. Distributed via email to AO Spine members globally, the questionnaire collected responses from March 20 to April 14, 2025.Results399 responses were received from mostly orthopedic surgeons, 28% from Asia Pacific, 27% from Europe and South Africa, 18% from Latin America, 16% from North America, and 11% from the Middle East and North Africa. Of the 279 respondents focused on deformity surgery, most utilized classification tools, clinical guidelines, and multicenter study findings for decision-making. However, over half were unaware of past Knowledge Forum Deformity resources, indicating a dissemination gap. Key sources of information were publications, conferences, and AO Spine courses. Major barriers to implementing new tools included workflow integration and administrative challenges. Top research priorities were the development of diagnostic and treatment guidelines (71%) and prediction algorithms (69%). Prominent knowledge gaps included proximal junctional kyphosis (PJK) (69%), spinal alignment guidelines (54%), and osteoporosis management (52%).ConclusionThe survey highlights PJK, spinal alignment, and osteoporosis as the leading knowledge gaps. Priorities for future research include developing guidelines and prediction tools. Improved dissemination strategies are needed to enhance clinical adoption and inform ongoing research and education initiatives.

Metal-binding sites (MBSs) are critical determinants of protein stability and biological function, yet methods for comparing their local binding environments lag behind those for whole-structure alignment. Here, we represent MBSs as atomic point clouds surrounding bound metal ligands and align them with a fine-tuned iterative closest point algorithm. Applying this framework to a redundancy-reduced collection of MBSs derived from all metalloproteins in the Protein Data Bank (PDB), we perform pairwise alignments across 23,342 sites to construct a similarity network of metal-binding environments. The resulting network topology recapitulates metal coordination chemistry and enzyme function: links are strongly enriched within metal types and across shared EC subclasses. Conserved metalloenzyme families form cohesive subnetworks; for example, the binuclear ureohydrolase domain appears as two tightly connected components that also capture atypical members such as the dinickel metformin hydrolase. We observe only a moderate global association between protein sequence and MBS geometry, yet many network links connect near-identical binding-site architectures across proteins with low sequence identity, consistent with either divergent evolution with local MBS conservation or candidate cases of molecular convergent evolution. Integrating network proximity with structural evidence of drug binding identifies drugs with enriched connectivity among their targets and predicts 528 drug-off-target combinations across 88 drugs and 151 human proteins, recovering both known off-targets (e.g., ADAM/ADAMTS for matrix metalloproteinase inhibitors) and proposing novel ones. The MBS network thus provides a scalable resource for probing metalloprotein evolution, functional convergence, and the structural basis of drug cross-reactivity.

ObjectiveThis study aimed to evaluate the quality and reliability of dengue fever-related videos on social media platforms.MethodsA cross-sectional study was conducted on January 10, 2026. Top 120 videos from each platform (YouTube and Douyin) were screened. Video characteristics were extracted. Quality and reliability were assessed using the Global Quality Scale (GQS), modified DISCERN, JAMA Benchmark criteria, and the Content Completeness Score (CCS).Results170 videos (70 from YouTube, 100 from Douyin) were included. YouTube videos were longer (118.50 vs. 78.50, Z = -3.79, p < 0.001), had higher CCS (8.00 vs. 6.00, Z = -3.50, p < 0.001) and mDISCERN scores (3.00 vs. 2.50, Z = -3.74, p < 0.001). YouTube videos were dominated by institutions (91.43%), followed by 8.57% healthcare professionals (HCPs), whereas nearly one third of contributors on Douyin were HCPs (31.00%). Associations between engagement and quality indicators were generally weak and varied across metrics, while engagement indicators on Douyin were strongly intercorrelated.ConclusionsYouTube provided more comprehensive and reliable information, whereas Douyin content was shorter and less comprehensive. Both platforms showed inconsistent alignment between popularity and informational quality, with this disconnect appearing more pronounced on Douyin.

Spectral clustering provides a principled framework for partitioning data through graph Laplacian optimization, yet its integration into deep multi-view learning remains fundamentally challenging due to hard orthogonality constraints, numerical instability, and the lack of principled mechanisms for fusing multiple spectral operators. In this paper, we propose Multi-View Spectral Contrastive Learning (MSCL), a theoretically grounded framework that unifies contrastive learning and spectral clustering from a spectral-operator perspective. MSCL jointly optimizes multiple view-specific spectral embeddings together with a consensus spectral embedding, and introduces a spectral contrastive regularization to align them in the spectral domain. A key component of MSCL is a differentiable Newton-Schulz orthogonalization module, which, for the first time, is introduced into deep spectral clustering to enforce hard orthogonality without explicit matrix decomposition or inversion, leading to numerically stable optimization and smooth gradient propagation. We further show that the proposed contrastive loss admits an interpretation as Laplacian smoothing on a kernel-induced similarity graph, establishing a principled connection between contrastive learning and spectral optimization. Extensive experiments on twelve benchmark datasets demonstrate that MSCL consistently outperforms state-of-the-art multi-view clustering methods, while additional analyses on orthogonalization stability and kernel choice further validate its robustness and scalability.

The soft tissue envelope within the residual limb influences prosthetic fit, function, and user comfort. The purpose of this study was to examine the influence of residual limb tissue composition and skeletal alignment on its mechanical properties during axial loading. We hypothesized that deviations in the adipose and muscle volume of the limb, as well as frontal plane alignment, will alter the mechanical stiffness and energy loss. We constructed multi-material synthetic residual limb models with varying tissue compositions and mechanically tested them under cyclical axial compression. Stiffness was quantified as the change in force divided by the change in displacement over the entire loading response, and energy loss was quantified as the mechanical hysteresis of the force-displacement loop. A large (~35%) reduction in soft tissue volume, representative of the change from pre- to post-limb recontouring surgery, resulted in an increase in stiffness of 57.6% out-of-socket and 17.3% in-socket relative to the pre-surgery model. Small (±2.5%) changes in tissue composition produced up to an 18.6% change in stiffness and up to a 59.2% change in hysteresis relative to the nominal post-surgery model. Finally, alignment in the frontal plane (±4° and ±8°) had significant effects on stiffness and hysteresis both pre- and post-surgery. We expect that clinical and technical approaches that optimize these factors could potentially contribute to improvements in whole-body outcomes such as metabolic cost and user comfort. In addition, these testing and verification methods offer a non-invasive and powerful technique to test patient-specific residual limb properties.

Gaussian Splatting has emerged as a prominent technique in computer vision and graphics, enabling high-fidelity scene reconstruction from a vast collection of 3D Gaussian primitives. Each primitive's appearance is governed by a viewindependent opacity parameter and a view-dependent color derived from spherical harmonics. A fundamental challenge, however, lies in the non-unique and heterogeneous nature of this parameterization, which limits physical plausibility and degrades expressiveness. To address this, we introduce the Extinction Coordinator for Gaussian Splattings (ECGS), which enforces a crucial consistency: a Gaussian's intrinsic opacity must align with its maximum alpha blending weight observed across all views. This constraint effectively encourages primitives to distribute along thin, surface-like shells, thereby enhancing the representation's expressiveness. Furthermore, we propose an anisotropic morphology regularization to promote planar Gaussian shapes while suppressing elongated, needle-like artifacts, leading to a more compact model. Extensive experiments on multiple benchmarks demonstrate that our approach reduces the Gaussian count by up to 75% while simultaneously boosting geometric accuracy and rendering speed. As a lightweight and modular component, ECGS can be seamlessly integrated into existing Gaussian Splatting pipelines. Code is available at: https://anonymous.4open.science/r/ECGS-F774.

Generalizable vehicle re-identification (ReID) seeks to develop models capable of adapting to previously unseen domains without additional fine-tuning or retraining. Most existing approaches attempt to learn domain-invariant representations by aligning data distributions across source domains. However, they often neglect the inherent domain-related redundancy within source images, which suppresses the learning of complementary features characterized by lower occurrence probabilities and weaker activations. To overcome this limitation, we introduce Unity in Diversity (UID), a framework of multi-expert knowledge adversarial learning and collaboration. UID incorporates a training-free mechanism to filter out domain-related redundancy in source images, thereby promoting the learning of complementary feature representations. Specifically, we design a Spectrum-based Transformation for Redundancy Elimination and Augmentation Module (STREAM), which generates two distinct types of image inputs for a two-stage complementary feature learning process. In the multi-expert knowledge adversarial learning phase, STREAM enables the model to acquire a diversified identity-oriented prompt set that captures subtle but discriminative visual cues critical for distinguishing highly similar vehicles. This multi-expert prompt set is progressively integrated into complementary feature representations through the proposed knowledge confrontation and collaboration mechanism, which substantially enhances the model's ability to extract fine-grained and complementary information. Extensive experiments conducted on multiple benchmarks demonstrate that UID achieves state-of-the-art performance, validating its effectiveness and generalizability. Our code is available at https://github.com/KZYYYY/UID.

RGBE tracking aims to integrate modality-aligned RGB and event features to perform temporal identity association and spatial localization of targets. Existing methods do not effectively mitigate the modality gap between RGB frames and event data. In response, we develop a hierarchical spatio-temporal alignment framework tailored to RGBE tracking. Spatially, our framework introduces Global Indirect Alignment (GIA) and Local Direct Alignment (LDA). GIA establishes modality-level alignment by projecting RGB and event backbones into a unified CLIP-based semantic space. In parallel, LDA enforces target-region matching between template and search regions to ensure precise spatial alignment. By coupling the coarse modality-level semantic correspondence from GIA with the fine-grained target-region correspondence from LDA, we establish a spatial alignment hierarchy from global cross-modal semantics to local target discrimination under CLIP's textual guidance. TIMF further extends this hierarchy from spatial alignment to temporal-invariance alignment. By aligning predicted invariant features with features sampled from the same target sequence, it preserves identity consistency across frames and provides a temporal prior that is adaptively fused with current observations. Extensive evaluations on three benchmarks show state-of-the-art success rates: COESOT 67.0%, VisEvent 60.6%, and FELT 46.9%, with consistent improvements across both standard and challenging scenarios. Our source code is available at https: //github.com/RiverUp/TextBridge-Track.