搜索结果：competitive

Our study evaluated long-term outcomes of nonoperative treatment of spondylolysis in adolescents and young athletes. Retrospective case series. High-volume sports medicine clinical and research center. Spondylolysis patients initially treated nonoperatively (10-25 year old; ≥5 years from diagnosis). We collected self-reported function (Oswestry Disability Index, ODI) and return-to-sport outcomes (for athletes) and compared ODI scores and follow-up between athletes and nonathletes, and between male and female patients. A total of 150 patients (133 competitive athletes; 108 males; mean age at diagnosis = 16.3 years; mean follow-up time = 8.0 years) were included. Bilateral defects were more common (75%) than unilateral defects (25%), and L5 was the primary affected level (63%). Nonoperative treatment approaches included activity modification (95%), physical therapy (93%), Nonsteroidal Anti-Inflammatory Drugs (NSAIDs) (64%), bracing (38%), and non-NSAID pain medication (23%). The mean ODI score at follow-up was 3.6 (0-100; best score = 0). ODI scores were higher (worse) in female patients versus male patients (P = 0.01), but did not statistically-differ between athletes and nonathletes (P = 0.29). Of competitive athletes, 91% returned to their preinjury sport at the same level or higher. Only 5 patients underwent subsequent back-related surgery after initial nonoperative treatment. We found excellent long-term outcomes after nonsurgical treatment of spondylolysis in a cohort of adolescents and young adults, including a large proportion of competitive athletes. Our findings underscore that nonsurgical interventions should be the mainstay treatment for spondylolysis in young individuals.

The 2022-2025 scoring system assigns higher difficulty to complex hybrid elements and acrobatics, which consequently necessitate prolonged underwater immersion. Therefore, it is hypothesized that maximizing scores through extended underwater exertion potentially exposes artistic swimmers to increased physiological demands. Since most quantitative data predates the 2022 rules, traditional routine evidence may underrepresent current competitive stress. This scoping review systematically map literature on artistic swimmers' physiological load and apnea characteristics, establishing a historical baseline to identify critical evidence gaps introduced by the 2022-2025 regulatory changes. Following PRISMA-ScR guidelines and the PCC framework, a systematic search across four major databases identified peer-reviewed original research reporting physiological load and/or apnea-related outcomes in competitive artistic swimmers. Synthesis of 36 articles revealed: (1) Routine execution triggers autonomic conflict (concurrent sympathetic/parasympathetic activation), producing broad heart rate ranges (~56.5-203.8 bpm) Additionally, chronic sport-specific training is linked to distinct cardiovascular adaptations. (2) Apnea duration in late traditional (pre-2022) routines constitutes 59%-64% of total performance time. Post-routine blood lactate concentrations are reported ~5.93-11.5 mmol/L. The revised regulations assign higher scoring weights to complex hybrid elements(officially defined as a combination of five or more lower-limb movements performed with intentional apnea),which aligns with increased underwater duration (Time Underwater, TU), higher anaerobic metabolic contributions, and instances of post-exercise hypoxemia. (3) The "Base Mark" penalty mechanism (the minimum degree of difficulty applied for non-conforming or failed elements) correlates with increased cognitive load. Studies also report stress-induced cortisol elevations and altered recovery of heart rate variability under these conditions. (4) Adolescent athletes exhibit still-developing anaerobic and cardiovascular systems. Consequently, based on legacy baselines, recent discussions hypothesize that younger athletes performing the high-frequency, prolonged underwater choreography promoted by the new scoring system may be exposed to increased acute hypoxic stress. Artistic swimming's exertion and prolonged apnea involve autonomic co-activation, high anaerobic demands, and specific psychophysiological responses. Based on theoretical extrapolation from established physiological baselines, it is hypothesized that the 2022-2025 scoring system increases athletes' exposure to cardiovascular and metabolic loading, presenting safety considerations for adolescents. Since most evidence predates 2022, longitudinal research under valid competitive conditions is necessary to assess the new regulations' physiological impacts. https://osf.io/dxapc/, identifier 10.17605/OSF.IO/DXAPC.

The cellular uptake and propagation of tau are central features of tauopathies, including Alzheimer's disease, and are mediated by the endocytic receptor low-density lipoprotein receptor-related protein 1 (LRP1). While prior studies have implicated LRP1 in tau binding and internalization, the biochemical features of this interaction and its suitability for therapeutic targeting remain incompletely defined. Here, we establish a quantitative and scalable framework to interrogate the tau-LRP1 interaction and identify small-molecule modulators. We engineered and purified the LRP1 ligand-binding domain 4 (BD4), a key region mediating tau interaction, and developed multiple orthogonal assays, including fluorescence polarization, split luciferase complementation, and time-resolved FRET, to measure LRP1-BD4 interactions with tau and a known peptide ligand. Across assay formats, we observe consistent binding affinities in the nanomolar range and demonstrate competitive displacement by tau, receptor-associated protein (RAP), and a peptide ligand, supporting overlapping binding interfaces. Leveraging these platforms, we performed small molecule high-throughput screening and identified a set of candidate inhibitors of the LRP1-BD4-tau interaction. Selected compounds reduced tau uptake in a cellular assay, phenocopying competitive inhibition by tau and a peptide ligand. Together, these studies define the LRP1-BD4-tau interaction as a biochemically tractable and druggable interface and establish an integrated discovery pipeline linking mechanistic characterization to functional cellular outcomes. This work provides a foundation for the development of therapeutic strategies targeting LRP1-mediated tau uptake.

High-throughput data-independent acquisition (DIA) workflows paired with short chromatographic separations are increasingly adopted for systems biology and clinical proteomics. However, narrower peak widths from rapid separations demand faster mass spectrometer cycle times to maintain quantitative depth and reproducibility. The synchro-PASEF acquisition mode on timsTOF mass spectrometers diagonally scans across ion mobility and m/z space, enabling efficient sampling of the precursor ion cloud with shortened cycle times. While synchro-PASEF has demonstrated competitive identification depth for global protein abundance samples compared to conventional dia-PASEF, its performance for phosphoproteomics-where the precursor ion cloud is characteristically broader and bimodally distributed-has not been evaluated. Here, we systematically optimized synchro-PASEF methods for phosphoproteomics and benchmarked performance against two dia-PASEF methods across three sub-hour separations. We found that synchro-PASEF performance depends critically on balancing diagonal window number, total isolation width, and gradient length, with longer gradients favoring more windows for selectivity and shorter gradients favoring fewer windows to preserve sampling frequency. An optimized configuration quantified over 19,000 localized phosphosites using a 23-minute separation. Retention time summation (RTsum) with a factor of 2 increased phosphopeptide identifications by 5-20% and reduced phosphosite-level coefficients of variation by up to 30% across all dia-PASEF and synchro-PASEF methods tested. Using β2-adrenergic receptor (B2AR) activation as a signaling model, we demonstrate that label-free DIA phosphoproteomics can be used to model phosphoproteomics dose-response relationships, showing that synchro-PASEF and dia-PASEF produce highly concordant phosphoproteomic responses, with comparable numbers of responding phosphosites, similar effect sizes, and nearly identical predicted protein kinase A (PKA) substrates downstream of the activated B2AR. While synchro-PASEF did not surpass optimized dia-PASEF in identification depth, its comparable biological performance and amenability to post-acquisition optimization through RTsum support its utility for high-throughput phosphoproteomics. This work provides a transferable framework for synchro-PASEF method optimization and demonstrates the broad utility of retention time summation for PASEF-based phosphoproteomics workflows. Systematic benchmarking of synchro-PASEF for typical phosphoproteomics workflows.RT summation improves IDs and quantitative precision for dia-PASEF and synchro-PASEFdia-PASEF and synchro-PASEF capture dose-response phosphosignaling with comparable performanceProvides transferable framework for high-throughput DIA method design.

ATP-competitive kinase inhibitors represent one of the largest classes of targeted anti-cancer drugs. While their primary mechanism is to block catalytic activity, they can also trigger paradoxical phenotypic effects that cannot be explained by catalytic inhibition alone. These observations point to a hidden layer of drug action that modulates non-catalytic kinase functions via changes in kinase conformation and protein-protein interactions (PPIs). Here, we developed a multimodal proteomics approach combining limited proteolysis coupled mass spectrometry on affinity-purified samples (AP-LiP-MS), AP-MS, and proximity labeling-MS to map inhibitor-induced conformation and PPI changes. We show that inhibitor binding causes structural rearrangements in the autoinhibitory domains (AIDs) of all tested kinases, consistent with a transition to an open, active-like kinase conformation. These structural shifts drive distinct kinase-protein interaction changes that control non-catalytic functions: sequestration of AMPK by inhibited CAMKK2 blocks phosphorylation by other kinases, CHEK1 inhibition causes dissociation from the mitochondrial protein CLPB and leads to mitochondrial fragmentation, and structural changes in inhibited PRKCA trigger rapid relocalization to cell junctions. Thus, we identify the ATP-binding site as a major organizing center of kinase conformation and interaction. Our work suggests that these on-target, off-mechanism effects are likely to occur in other kinases as well, and provides the analytical framework to systematically characterize a frequently overlooked phenomenon highly relevant for understanding drug side effects to guide the development of novel therapeutics.

The classification of Acute Lymphoblastic Leukemia (ALL) from peripheral blood smear images using Convolutional Neural Networks (CNNs) has achieved expert-level accuracy. However, the computational and memory requirements of CNNs pose a barrier to their deployment in resource-constrained clinical settings and low-income countries. To bridge this gap, we propose NeurALLNet, a memory-efficient convolutional spiking neural network (SNN) augmented with Squeeze-and-Excitation channel attention for the multi-class classification of ALL subtypes. NeurALLNet leverages sparse, event-driven temporal computation with an ultra-compact architecture of approximately 0.3M trainable parameters. The model was trained and evaluated on a primary dataset of ALL peripheral blood smear images, and its clinical generalizability was rigorously validated on an unseen external cohort of 3,242 images without retraining. We conducted hardware profiling on CPU and GPU platforms, alongside ablation studies and Grad-CAM visual explanations, to evaluate deployment viability and interpretability. NeurALLNet achieved a test accuracy of 98.16% on the primary dataset, with a bootstrapped 95% Confidence Interval (CI) of [0.9663, 0.9939]. On the external validation cohort, it yielded an accuracy of 96.02%, with a robust 95% CI of [0.9534, 0.9667]. The architecture requires a memory footprint of 1.35 MB, achieving single-image inference latencies of 454.67 ms on a standard CPU and 11.24 ms on a GPU. Ablation studies confirmed that the attention mechanism is critical to the network's discriminative power, and Grad-CAM visualizations verified that predictions are grounded in clinically relevant morphological features. Compared to recent state-of-the-art ensemble and hybrid CNNs that require millions of parameters, NeurALLNet delivers competitive diagnostic accuracy while reducing the computational footprint by orders of magnitude. By providing this precision within a 1.35 MB envelope, NeurALLNet offers a scalable, energy-efficient digital health intervention suitable for portable Lab-on-a-Chip devices and point-of-care diagnostics worldwide.

Therapeutic interventions for hematopoietic acute radiation syndrome (H-ARS) remain limited in both variety and efficacy, falling short of achieving durable recovery of the hematopoietic system. Here, we develop 2B9F, a novel thrombopoietin-mimetic peptide engineered for enhanced stability and designed with the intention of achieving low immunogenicity. A single subcutaneous dose of 2B9F administered postexposure achieved complete survival in a lethal murine irradiation model, surpassing the efficacy of the clinically approved agent romiplostim. Treatment with 2B9F accelerated the recovery of peripheral blood counts and bone marrow nucleated cells, expanded functional hematopoietic stem cells (HSCs), and enhanced both in vitro clonogenic potential and in vivo competitive repopulation capacity. Mechanistic studies revealed that 2B9F promoted HSC quiescence and survival through transcriptional modulation of apoptosis and cell-cycle regulators. Importantly, a single low dose of 2B9F also conferred durable protection, mitigating delayed bone marrow suppression for up to six months postirradiation. These findings position 2B9F as a promising single-dose, low-immunogenicity therapeutic candidate for H-ARS, indicating strong potential for clinical translation in nuclear emergencies.

Data are an engine of decisions, and in an industry such as healthcare, the consequences of decisions go beyond business sustainability to affect human lives. Hence, the data governance concept in healthcare organizations is vital and not only for compliance purposes. Thus, this review aims to provide an overview of the roles of data governance in achieving a sustainable healthcare organization by surveying the literature of the last decade. Fifteen studies were included; the roles span from the ability of data governance to transform organizations and help in achieving competitive advantages to boosting performance and effectiveness. Additionally, data governance has established roles in providing harmonization in processes and data. These roles are attributed mainly to flexible, scalable, and structured practices in managing data, which in turn lead to enhanced data flow, reuse of data, and hence interoperability across the organization. Unlike other industries, the governance of data in healthcare has yet to be well standardized, considering the complexity of processes and heterogeneity of data in healthcare. Therefore, building guidelines and best practices for governing data in healthcare is essential. Furthermore, stronger empirical studies are needed, considering the outcomes of data governance are context dependent.

The combinations of Convolutional Neural Networks (CNNs) and Transformer have shown promising results in many medical image segmentation tasks. However, the simple feature stacking or concatenation may neglect multi-scale semantic alignment, which induces semantic ambiguity during the decoding phase. Moreover, in many existing CNN and Transformer hybrid U-shaped segmentation models, the skip connections directly transfer features within corresponding network layers, which may introduce unexpected noise and undermine the global representation advantage of Transformer. In this paper, we introduce MAFR-UNet, a model that effectively integrates CNN and Transformer to synergize their local and global feature extraction capabilities. Specifically, the backbone is enhanced by a Multi-scale Adaptive Feature Reassembly (MAFR) module to capture and align multi-level features. Meanwhile, a residual CNN bottleneck module is incorporated to strengthen local feature extraction while reducing computational complexity. To address the ambiguity of target boundaries in CTA images, we incorporate a specific boundary-aware loss term into the objective function, thereby explicitly enforcing sharper boundary delineation. Experimental results demonstrate that the MAFR-UNet achieves competitive segmentation performance. Furthermore, the model exhibits good generalization capabilities across multi-organ segmentation tasks, highlighting its potential for diverse clinical applications. The source code and implementation details of MAFR-UNet are available at https://github.com/xnwn/MAFR-UNet.

Recurrent glenohumeral dislocation is a major challenge in sports medicine. Although the Latarjet procedure is often the gold standard for high-risk recurrence, it is associated with bone-block complications and high invasiveness. This study aims to evaluate the clinical and functional outcomes of a novel and less invasive conjoint tendon augmentation technique compared to the traditional Latarjet procedure. We primarily hypothesized that there would be no significant difference in clinical stability and functional outcomes between the 2 procedures at a minimum 2-year follow-up. Additionally, we aimed to evaluate the safety profile regarding bone-related complications. A retrospective comparative study was conducted on patients with recurrent glenohumeral dislocation treated between June 2014 and November 2019. Patients were stratified into 2 groups based on the surgical approach. Functional outcomes were assessed using the University of California at Los Angeles Activity score, Constant Murley score, and Rowe score. Post-operative ultrasound (US) evaluated tendon kinematics and the dynamic "hammock effect." Range of motion and return to sport rates were also documented. A total of 53 patients were included (mean age 28.0 ± 3.22 years; range 18-38; mean follow-up 4.2 ± 0.8 years). The cohort comprised 38 males (72%) and 15 females (28%). The mean number of pre-operative dislocation episodes was 4 ± 1.6 (range: 2-9). All patients practiced sports >2 times/wk, including 11 (20.8%) competitive athletes. No significant differences were found between groups regarding post-operative clinical scores (University of California at Los Angeles, Constant Murley score, Rowe), return to sports, range of motion, or reoperation rates. US analysis confirmed proper dynamic tensioning of the conjoint tendon in the novel group, successfully replicating the stabilizing hammock effect. The novel conjoint tendon augmentation technique demonstrated clinical and functional noninferiority to the Latarjet procedure. This approach represents a bone-preserving alternative to the Latarjet procedure, avoiding coracoid osteotomy and hardware implantation. Dynamic US confirmed a reliable hammock effect. Conjoint tendon augmentation is a viable alternative for patients without significant bony Bankart lesions who do not require revision surgery.

Panax vietnamensis is a medicinally valuable species whose wild populations are threatened by overexploitation, necessitating the development of sustainable, high-yielding cultivation systems. A six-year systematic assessment was conducted on three artificial cultivation models: standard field (SF), artificial forest (AF), and wild forest (WF). Growth parameters, biomass production, and saponin content (via HPLC-ELSD) were measured. Furthermore, non-targeted metabolomic analysis (LC-MS/MS) was employed to elucidate the metabolic mechanisms underlying yield and quality in SF-grown plants. The SF model produced the highest biomass at year five, significantly outperforming the AF and WF systems, while achieving competitive total saponin content.Metabolomic profiling identified the fifth year as a pivotal developmental transition, characterized by the coordinated upregulation of secondary metabolite biosynthesis and stress adaptation pathways, alongside a significant enrichment of the key saponin precursor dimethylallyl diphosphate in taproots. Moreover, a functional division of labor was observed: rhizomes primarily synthesized precursors, whereas taproots functioned as the primary sink for saponin storage, indicating an efficient resource allocation strategy. This study establishes the five-year SF model as a scientifically grounded and sustainable cultivation protocol for P. vietnamensis. These findings advance our understanding of how cultivation-driven metabolic shifts mediate quality enhancement, providing a framework that integrates agricultural productivity with species conservation.

Tumor cells must occupy and thrive in a competitive microenvironment marked by limited metabolites, including essential amino acids like methionine. Using a leukemia suppression model and CRISPR screening, we found that the choline transporter SLC44A1 is overexpressed in leukemia patients and impacts leukemogenesis. Choline is an important nutrient for membrane synthesis and less commonly contributes to the methionine cycle. A metabolic analysis demonstrated that metabolites of the methionine pathway are significantly elevated in leukemic cells. Surprisingly, dietary restriction of methionine accelerated leukemogenesis in vivo. Choline can serve as an alternative source for methionine via the enzymatic activity of CHDH and BHMT. Under restrictive methionine conditions, BHMT and CHDH are significantly upregulated. In vivo, BHMT and CHDH are necessary for leukemia progression where they utilize choline as an alternative source to satisfy increased methionine demand. This pathway represents a vulnerability in cancer cells that may be exploited for therapeutic intervention.

The increasing presence of toxic lead ions (Pb2+) in water resources poses a serious threat to human health and the environment, necessitating the development of efficient and reusable adsorbents. In this study, a novel poly(Allyl Glycidyl Ether-co-Iminodiacetic Acid)-Maleic Anhydride (poly(AGE-co-IDA)-MBAm) polymeric adsorbent was synthesized, immobilized on nonwoven fabric, and systematically characterized by FT-IR, NMR, CHN, XRD, BET, SEM, and TGA analyses. Batch adsorption experiments demonstrated that Pb2+ removal was highly pH-dependent, with an optimum adsorption capacity of 250 mg g-1 achieved at pH 7. The adsorption equilibrium was reached within 60 min, and kinetic modeling confirmed the pseudo-second-order model (R 2 = 0.9799). Isotherm analysis indicated excellent agreement with the Freundlich model (q max = 99.38 mg g-1, R 2 = 0.99.385), suggesting monolayer adsorption on a homogeneous surface. The adsorbent showed high selectivity for Pb2+ ions in the presence of common interfering cations, with Fe3+ displaying the strongest competitive effect. Regeneration studies using 0.5 M H2SO4 revealed more than 89% recovery efficiency after 65 cycles, confirming its long-term stability and reusability. Practical applicability was further validated through successful treatment of wastewater from a battery manufacturing plant containing 42.3 mg L-1 Pb2+.

The rapid, nondestructive assessment of tamarind quality is crucial for grading, pricing, and industrial standardization. Therefore, the purpose of this study is to classify and predict intact tamarind quality based on soluble solid content (SSC) by combining short-wave infrared (SWIR) spectroscopy with variable selection algorithms and chemometric models. SWIR absorbance spectra (900-1700 nm) were acquired from 120 samples of intact tamarind. Partial least squares-discriminant analysis (PLS-DA) and partial least squares-regression (PLS-R) models were developed using full spectra and various spectral preprocessing techniques to classify and predict SSC of tamarind, respectively, and the developed models were evaluated. The multiplicative scatter correction (MSC) and Savitzky-Golay (SG) second derivatives were selected as the best options for classification and prediction, respectively. Then, competitive adaptive reweighted sampling (CARS), the successive projections algorithm (SPA), and variable importance in projection (VIP) were applied to select effective wavelengths for developing simplified, more robust classification and prediction models for intact tamarind based on SSC. The PLS-DA-CARS model using MSC preprocessing achieved the best classification results with 97.2% accuracy, whereas PLS-R-CARS model with range normalization to predict SSC yielded a coefficient of prediction (Rp) of 0.85 and standard error of prediction (SEP) of 2.488%Brix.

Metal-organic framework (MOF) membranes have attracted increasing interest for energy-efficient gas separation due to their tunable pore structures and selective adsorption properties. In this study, CAU-1-NH2 membranes were fabricated on porous α-alumina substrates via a seeded growth method. The effects of precursor concentration and ligand-to-metal ratio in the secondary growth solution on membrane morphology and gas separation performance were systematically investigated. Among the five membrane variants synthesized, the optimized CAU-1-NH2(B) membrane exhibited a continuous and low-defect structure, as confirmed by scanning electron microscopy and confocal fluorescence microscopy. Single-gas permeation measurements on the CAU-1-NH2(B) membrane at 35 °C and 3 bar showed H2, CO2, N2, and CH4 permeances of 207.1, 110.2, 6.0, and 6.6 GPU, respectively, corresponding to ideal CO2/N2 and CO2/CH4 selectivities of 19.4 and 17.6. Mixed-gas permeation tests revealed significantly enhanced separation performance, with CO2/N2 separation factors ranging from 59.3 to 89.2 under various feed compositions. Grand canonical Monte Carlo (GCMC) simulations further indicate that strong competitive adsorption of CO2 within the CAU-1-NH2 framework plays a dominant role in governing the observed mixed-gas separation behavior. These findings demonstrate the potential of CAU-1-NH2 membranes for efficient CO2 separation.

The efficient separation of benzene (C6H6) from cyclohexane (C6H6) remains a longstanding challenge due to their similar molecular structures and nearly identical boiling points. Herein, we report a supramolecular liquid-liquid extraction strategy based on a water-soluble dicyclohexyl-substituted cucurbit[6]uril (Cy2Q[6]) for this demanding separation. Spectroscopic studies reveal that Cy2Q[6] can encapsulate both guests individually, while competitive binding experiments demonstrate a pronounced preference for C6H6 in binary systems. Density functional theory (DFT) calculations further confirm the higher thermodynamic stability of the Cy2Q[6]⊃C6H6 complex compared to Cy2Q[6]⊃C6H12. Exploiting this selective molecular recognition, complete separation of an equimolar C6H6/C6H12 mixture is achieved in a single extraction step under ambient conditions, affording quantitative selectivity toward C6H6. The aqueous Cy2Q[6] phase exhibits excellent recyclability with minimal performance loss over multiple cycles. Furthermore, simulated industrial experiments demonstrate efficient removal of trace C6H6 impurities, highlighting the practical applicability of this system. This work establishes a sustainable supramolecular extraction paradigm and provides an energy-efficient alternative to conventional distillation for the separation of structurally similar hydrocarbon mixtures.

This study presents an enhanced approach for recovering the secret key of the standard classical transposition cryptosystem using a recently introduced metaheuristic, the coati optimization algorithm. To adapt the coati optimization algorithm for cryptanalysis, its core framework is specifically customized for the considered transposition cryptosystem. The performance of the tailored coati optimization algorithm is benchmarked against three established metaheuristics, namely genetic algorithm, cuckoo search and particle swarm optimization based on accuracy, effectiveness, and efficiency. Experimental results demonstrate that the proposed method performs competitively, often surpassing genetic algorithm, cuckoo search, and particle swarm optimization across all evaluation metrics. However, the current experimental evaluation is limited to English-language ciphertexts using English n-gram statistics; therefore, the applicability of the proposed framework to multilingual cryptanalysis remains a direction for future investigation.

To translate and cross-culturally adapt the University of Wisconsin Running Injury and Recovery Index (UWRI) into Arabic (UWRI-Ar) and evaluate its psychometric properties in Arabic-speaking runners with running-related injury (RRI). A cross-cultural adaptation study with cross-sectional psychometric evaluation and test-retest reliability assessment was conducted across multiple physical therapy clinics using online survey administration. Eighty-three Arabic-speaking recreational or competitive runners aged 18-45 years with running-related injuries completed the UWRI-Ar, and 49/83 of participants (i.e., 59%) completed a retest after 2-5 days. Measurement properties evaluated included structural validity using exploratory factor analysis, internal consistency, floor and ceiling effects using the 15% threshold, and test-retest reliability with calculation of the standard error of measurement (SEM) and smallest detectable change (SDC). Construct validity was examined against the Lower Extremity Functional Scale (LEFS), Numeric Pain Rating Scale (NPRS), Tampa Scale of Kinesiophobia (TSK-17), and Pain Catastrophizing Scale (PCS) using Spearman correlations. Exploratory factor analysis supported a one-factor solution (eigenvalue&#x2009;=&#x2009;4.46), explaining 49.5% of variance with item loadings 0.45-0.87. No floor and ceiling effects were observed. Internal consistency was good (Cronbach's &#x3b1;&#x2009;=&#x2009;0.889; and McDonald's &#x3c9;&#x2009;=&#x2009;0.891). Test-retest reliability was good (ICC[2,1]&#x2009;=&#x2009;0.801; 95% CI 0.673-0.882), with SEM&#x2009;=&#x2009;3.55 and SDC&#x2009;=&#x2009;9.83 (individual) and 1.40 (group). UWRI-Ar correlated positively with LEFS (&#x3c1;&#x2009;=&#x2009;0.488) and negatively with NPRS (&#x3c1; = -0.263), TSK-17 (&#x3c1; = -0.463), and PCS total (&#x3c1; = -0.308) (all p < .05). The UWRI-Ar demonstrates acceptable structural validity, reliability, and construct validity, supporting its use as a brief, running-specific patient-reported outcome measure for short-term monitoring of recovery progression from running-related injury in Arabic-speaking runners.