Abstract:

Major depressive disorder (MDD) represents a heterogeneous condition lacking reliable neurobiological biomarkers and mechanistic understanding. Time-resolved characterisation of brain dynamics reveals that mental health is associated with a characteristic dynamical regime, exhibiting spontaneous switching between a repertoire of ghost attractor states forming resting-state networks. Analysing resting-state fMRI data from 848 MDD patients and 794 healthy controls across 17 sites in China (REST-meta-MDD) using Leading Eigenvector Dynamics Analysis (LEiDA), we found MDD patients exhibit significantly reduced default mode network (DMN) occupancy (p < 0.001; Hedges' g = −0.51) and increased occipito-parieto-temporal state occupancy (p < 0.001; Hedges' g = 0.42), suggesting compensatory dynamical rebalancing. These findings extend prior observations of disrupted DMN in MDD, aligning with the emerging dynamical systems framework for mental health to advance mechanistic understanding of MDD pathophysiology.

Abstract: This work compares two common approaches for classifying schizophrenia from EEG data—EEGNet, a compact convolutional neural network, and a Random Forest trained on spectral features—with an emphasis on how well they generalize across datasets. The models were trained on the ASZED-153 dataset using subject-level stratified cross-validation and then evaluated on a completely separate Kaggle EEG dataset collected under different recording conditions. While internal validation appeared reasonably encouraging (70.7% accuracy for EEGNet and 66.8% for Random Forest), performance dropped sharply on the external dataset (54.6% and 45.4%, respectively). This 16–21 percentage point decline was consistent with Maximum Mean Discrepancy results (MMD=0.0914), indicating meaningful distribution differences between datasets. A simple domain adaptation attempt (correlation alignment) provided only a modest improvement (about +1.2 percentage points) and did not recover internal performance levels. Overall, these findings highlight the practical challenge of developing EEG-based classifiers that remain reliable across recording sites and underscore the importance of external validation and more robust cross-site training strategies before considering any clinical deployment.

Abstract: The accumulation of polyethylene (PE) waste presents significant environmental and economic challenges, particularly in developing regions where plastic valorisation infrastructure remains limited. In this work, waste polyethylene was upgraded through coordination-catalyzed oxidative functionalization using earth-abundant Schiff base metal complexes of iron, cobalt, manganese, and copper with salen and salophen ligands. The process enables selective incorporation of oxygen-containing functional groups while largely preserving polymer molecular integrity, offering a material-oriented alternative to fuel-focused plastic recycling. Fourier transform infrared spectroscopy confirmed the formation of carbonyl and hydroxyl functionalities, with the carbonyl index (CI) increasing from 0.02 ± 0.01 for untreated polyethylene to 0.48 ± 0.04 and 0.42 ± 0.03 for Fe(salen)Cl and Co(salen) catalysts, respectively, under identical conditions. Salophen-based complexes consistently exhibited slightly higher oxidation efficiencies than their salen analogues. Gel permeation chromatography revealed controlled molecular weight reduction, with number-average molecular weight (Mₙ) decreasing from 62.4 × 10³ g•mol⁻¹ (untreated PE) to 56.8 × 10³ and 54.9 × 10³ g•mol⁻¹ for Fe- and Co-based systems, while dispersity remained within polymer-grade ranges. Differential scanning calorimetry and thermogravimetric analysis showed only minor changes in melting temperature and thermal stability. Surface-sensitive X-ray photoelectron spectroscopy confirmed oxidation localized primarily at the polymer surface, while atomic absorption spectroscopy indicated residual metal contents below 10 ppm. Catalyst reusability studies demonstrated sustained activity over multiple cycles. Overall, this coordination-catalyzed strategy provides a scalable and industrially relevant pathway for upgrading polyethylene waste into value-added functional polymers, with strong potential for integration into emerging circular polymer economies in Nigeria and other African regions.

Abstract: This study introduces a novel neural network-based symbolic computation algorithm (NNSCA) for obtaining exact solutions to the (3+1)dimension Jimbo-Miwa equation. By integrating neural networks with symbolic computation, NNSCA addresses the limitations of conventional approaches, enabling the derivation and visualization of exact solutions. The neural network architecture is meticulously designed, and the partial differential equation is transformed into algebraic constraints via Maple, establishing a closed-loop solution framework. NNSCA offers a generalized paradigm for investigating high-dimensional nonlinear partial differential equations, highlighting its substantial application prospects.

Abstract: Background: Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of advanced non-small cell lung cancer (aNSCLC). However, immune-related adverse events (irAEs) remain a clinical challenge in this context. Genetic variants acting as cis-eQTLs may predict toxicity risk, thereby enabling personalized treatment. We investigated the association between the IL7 rs16906115 polymorphism, adverse events (AEs), and survival outcomes in patients with aNSCLC receiving ICIs. Methods: This retrospective cohort study analyzed 153 patients with aNSCLC treated with ICIs (2018–2023) at two centers in Spain. The final analytical cohort included 124 patients with complete clinical follow-up. IL7 rs16906115 genotyping was performed using TaqMan assays. Associations between genotypes/alleles, AEs, and survival (PFS/OS) were evaluated using logistic regression and Kaplan-Meier analysis. A clinical-genetic predictive model was developed. Results: The minor A allele frequency was 8.5%. Carriers of the A allele (AG/AA genotypes) had significantly higher adverse event rates compared to GG homozygotes (OR = 3.77, 95% CI: 1.16–12.6, p = 0.0081). The as-sociation remained significant after multivariable adjustment (OR = 4.64, 95% CI: 1.50–17.2, p = 0.0203). Crucially, A-allele carriers exhibited significantly shorter Pro-gression-Free Survival compared to non-carriers (median 6.6 vs. 10 months, p = 0.0029). The combined clinical-genetic model achieved superior predictive perfor-mance for toxicity (AUC = 0.67) compared to clinical-only models (AUC = 0.57), suc-cessfully stratifying patients into moderate- and high-risk groups, respectively. Conclusions: IL7 rs16906115 polymorphism represents a potential pharmacogenetic bi-omarker for predicting adverse events and identifying patients with poor prognosis in aNSCLC immunotherapy. Incorporating genetic profiling into clinical practice may enable personalized toxicity monitoring and enhance treatment safety using precision medicine.

Abstract: Patient satisfaction is crucial to aesthetic surgery, yet measuring how well outcomes meet patient expectations has always been challenging. Rather than relying on the surgeon’s impression, we’ve synthesized research on Patient-Reported Outcome Measures (PROMs) in facial aesthetics. Our work zeroes in on the FACE-Q instrument and explores newer technological applications. We conducted a comprehensive literature review of studies on facelifts (563 patients across 10 studies), injectable treatments (2292 patients in 23 studies), and rhinoplasty (937 patients across 10 studies). Our original data came from a Dutch cohort of Clinique Rebelle in Amsterdam—259 patients undergoing facial procedures, supplemented by Computerized Adaptive Testing (CAT) simulation research. The FACE-Q scales demonstrated strong psychometric properties—Cronbach’s alpha between 0.885 and 0.951—and successfully captured differences between patients that traditional photos miss. CAT methods reduced questionnaire length by roughly 71% without sacrificing measurement accuracy (r = 0.98 with complete surveys). Looking ahead, machine learning shows real potential for forecasting patient satisfaction outcomes. Implementing routine PROM collection in aesthetic practice makes sense on multiple fronts: better patient selection, benchmarking quality across surgeons, protecting against medicolegal concerns, and aligning with value-based healthcare models. We also discuss how AI and 3D imaging might reshape outcome assessment going forward.

Abstract:

The optimal dietary balance between n‑6 and n‑3 polyunsaturated fatty acids (PUFAs), the safe upper intake of n‑6 PUFAs—particularly linoleic acid—and the physiological consequences of their metabolic competition remain unresolved in the context of the Western diet. Since the 1980s, Bill Lands and colleagues have argued that high n‑6 PUFA intake can shift the balance of n‑3–derived pathways and eicosanoid signaling, potentially influencing processes relevant to non‑communicable diseases. Despite its potential public‑health implications, this hypothesis has received limited systematic attention. In this narrative review, we synthesize key aspects of Lands’ work, evaluate supportive and contradictory evidence, and highlight mechanistic insights into lipid competition and inflammatory regulation. We conclude that these unresolved but testable hypotheses warrant renewed investigation, as their corroboration could reshape dietary guidelines and strategies for chronic disease prevention.

Abstract: Photodynamic therapy (PDT) offers a promising complementary strategy for the treatment of glioblastoma multiforme (GBM); however, achieving selective activation in tumor tissue and maintaining efficacy under hypoxic conditions remain significant limitations. In this study, we present the synthesis and functional evaluation of Gal-SiX, an enzymatically activatable Si-xanthene photosensitizer designed to address these challenges. Prepared through an improved 10-step synthetic route, Gal-SiX displays a clear turn-on fluorescence and absorbance response upon β-galactosidase activation and generates reactive oxygen species efficiently in aqueous media. Mechanistic studies revealed that Gal-SiX enables both Type I and Type II PDT pathways, an advantageous feature for GBM, where oxygen availability is restricted. In vitro assays conducted on U87MG glioblastoma cells and L929 healthy fibroblasts demonstrated meaningful selectivity, with IC50 values of 3.30 μM and 7.19 μM, respectively. Gal-SiX also showed minimal dark toxicity (>80 μM) and potent light-induced cytotoxicity, yielding a phototoxicity index of 24.8 in glioblastoma cells. Confocal imaging and MTT assays consistently demonstrated its activation and PDT efficacy. Overall, this work introduces the first activatable Si-xanthene–based PDT agent for glioblastoma and provides the first evidence that the Si-xanthene scaffold can support dual Type I/II phototoxicity. These results underscore Gal-SiX’s potential as a selective PDT platform for addressing the unique constraints of GBM biology.

Abstract: While chronic alcohol consumption is an established risk factor for lipid metabolic dysregulation, the underlying genetic mediators remain largely elusive. This study investigated the synergistic impact of CCDC63 (Coiled-Coil Domain Containing 63) polymorphisms and alcohol intake on dyslipidemia risk within a Korean cohort. Leveraging data from the KARE study (n=6,655; 4,327 dyslipidemia cases vs. 2,328 controls), we analyzed SNPs across the CCDC63 locus via Affymetrix SNP Array 5.0. Logistic regression, adjusted for age and sex, was performed to evaluate genotype-phenotype association, and gene-environment interactions by alcohol exposure duration. Three intronic variants (rs10849915, rs11065756, and rs2238149) were significantly associated with dyslipidemia (OR ≥1.15, P <0.005). Notably, stratified analysis revealed a clear gene–environment interaction. In current drinkers, the G-allele of rs10849915 was significantly associated with a higher risk of dyslipidemia (OR = 1.23, P <0.05) , significantly lower γ-GTP levels (β =-8.08), and reduced HDL (β =-1.42). However, no such genetic associations were observed in the non-drinking group (P > 0.05 for all traits). Our findings demonstrate that CCDC63 variants specifically modulate lipid metabolism and hepatic enzyme levels in an alcohol-dependent manner. The paradoxical association—lower γ-GTP yet higher dyslipidemia risk in drinkers—suggests that CCDC63 plays a critical role in the complex interplay between alcohol exposure and systemic lipid homeostasis.

Abstract: Current paradigms in Self-Supervised Learning (SSL) achieve state-of-the-art results through complex, heuristic-driven pretext tasks such as contrastive learning or masked image modeling. This work proposes a departure from these heuristics by reframing SSL through the fundamental principle of Minimum Description Length (MDL). We introduce the MDL-Autoencoder (MDL-AE), a framework that learns visual representations by optimizing a VQ-VAE-based objective to find the most efficient, discrete compression of visual data. We conduct a rigorous series of experiments on CIFAR-10, demonstrating that this compression-driven objective successfully learns a rich vocabulary of local visual concepts. However, our investigation uncovers a critical and non-obvious architectural insight: despite learning a visibly superior and higher-fidelity vocabulary of visual concepts, a more powerful tokenizer fails to improve downstream performance, revealing that the nature of the learned representation dictates the optimal downstream architecture. We show that our MDL-AE learns a vocabulary of holistic object parts rather than generic, composable primitives. Consequently, we find that a sophisticated Vision Transformer (ViT) head, a state-of-the-art tool for understanding token relationships, consistently fails to outperform a simple linear probe on the flattened feature map. This architectural mismatch reveals that the most powerful downstream aggregator is not always the most effective. To validate this, we demonstrate that a dedicated self-supervised alignment task, based on Masked Autoencoding of the discrete tokens, resolves this mismatch and dramatically improves performance, bridging the gap between generative fidelity and discriminative utility. Our work provides a compelling end-to-end case study on the importance of co-designing objectives and their downstream architectures, showing that token-specific pre-training is crucial for unlocking the potential of powerful aggregators.

Abstract:

Traditional paradigms of nation-building and state-building have dominated political theory and international policy for decades, yet their explanatory and prescriptive power remains limited in postcolonial and conflict-affected contexts. Recurrent instability, institutional fragility, and governance failure are often interpreted as operational deficiencies, yet this article contends that the root cause is primarily epistemological. Existing frameworks fragment political life into discrete domains—institutions, identity, legitimacy—while remaining anchored in Westphalian assumptions that fail to capture the dynamic, adaptive nature of political communities. This article introduces Nationesis, a novel transdisciplinary science dedicated to the study of nations as living, adaptive systems whose persistence depends on regenerative processes rather than mere stabilization. Nationesis integrates insights from political theory, comparative constitutionalism, postcolonial scholarship, and systems science to provide a unified analytical framework encompassing institutions, collective meaning, historical memory, leadership intelligence, and legitimacy. Using the Democratic Republic of the Congo as a paradigmatic case of systemic complexity, the article demonstrates why conventional paradigms systematically misread patterns of persistence, fragility, and renewal. The study concludes that the future of political order relies not on institutional replication alone but on a community’s capacity to regenerate meaning, legitimacy, and collective coherence under systemic strain. Nationesis thus offers a transformative lens for political theory, global constitutionalism, and the science of sustainable political communities.

Abstract: We present a fully controlled thermodynamic study of the two-dimensional dipolar $Q$-state clock model on small square lattices with free boundaries, combining exhaustive state enumeration with noise-free evaluation of canonical observables. We resolve the complete energy spectra and degeneracies $\{E_n,c_n\}$ for the Ising case ($Q=2$) on lattices of size $L=3,4,5$, and for clock symmetries $Q=4,6,8$ on a $3\times3$ lattice, tracking how the competition between exchange and long-range dipolar interactions reorganizes the low-energy manifold as the ratio $\alpha = D/J$ is varied. Beyond a finite-size characterization, we identify several qualitatively new thermodynamic signatures induced solely by dipolar anisotropy. First, we demonstrate that ground-state level crossings generated by long-range interactions appear as exact zeros of the specific heat in the limit $C(T \rightarrow 0,\alpha)$, establishing an unambiguous correspondence between microscopic spectral rearrangements and macroscopic caloric response. Second, we show that the shape of the associated Schottky-like anomalies encodes detailed information about the degeneracy structure of the competing low-energy states: odd lattices ($L = 3,5$) display strongly asymmetric peaks due to unbalanced multiplicities, whereas the even lattice ($L = 4$) exhibits three critical values of $\alpha$ accompanied by nearly symmetric anomalies, reflecting paired degeneracies and revealing lattice parity as a key organizing principle. Third, we uncover a symmetry-driven crossover with increasing $Q$: while the $Q=2$ and $Q=4$ models retain sharp dipolar-induced critical points and pronounced low-temperature structure, for $Q \ge 6$ the energy landscape becomes sufficiently smooth to suppress ground-state crossings altogether, yielding purely thermal specific-heat maxima. Altogether, our results provide a unified, size- and symmetry-resolved picture of how long-range anisotropy, lattice parity, and discrete rotational symmetry shape the thermodynamics of mesoscopic magnetic systems. We show that dipolar interactions alone are sufficient to generate nontrivial critical-like caloric behavior in clusters as small as $3\times3$, establishing exact finite-size benchmarks directly relevant for van der Waals nanomagnets, artificial spin-ice arrays, and dipolar-coupled nanomagnetic structures.

Abstract: Turbidity, a key indicator of water quality, arises from suspended and colloidal particles that reduce clarity, hinder disinfection, disrupt aquatic ecosystems, and undermine consumer confidence. With increasing pressures from global water pollution, effective turbidity control is critical for protecting public health, supporting industrial operations, and maintaining environmental sustainability. It is also essential for the stable performance of water treatment processes, including biologically mediated systems such as slow sand filtration. A wide range of treatment techniques, spanning conventional approaches to advanced emerging technologies, are available for turbidity removal; however, existing reviews often consider these methods in isolation, limiting comparative insight. This review presents a mechanism-based classification framework that integrates both traditional and modern approaches. Treatment methods are classified according to their underlying mechanisms, including particle destabilization, aggregation, and separation; adsorptive and transformation processes; and hybrid or assisted systems that combine multiple mechanisms. For each category, the review examines fundamental principles, operational mechanisms, turbidity removal efficiencies, advantages, and limitations, supported by relevant case studies. A comparative discussion highlights the strengths and constraints of different methods, providing a comprehensive reference to guide the selection and optimization of turbidity control strategies across diverse water matrices and treatment objectives.

Abstract: Motivated and inspired by Truesdell's seminal article [``Two measures of vorticity," Journal of Rational Mechanics and Analysis {\bf 2}, 173--217 (1953)], recently the present author has introduced the turbulence kinematical vorticity number $\widetilde{\cal V}_{K}$ to measure the mean rotationality of turbulence [``On the classical Bradshaw--Richardson number: Its generalized form, properties, and application in turbulence," Physics of Fluids {\bf 30}, 125110 (2018)]. In this work, first, within the general framework of the Cauchy equation of motion, we derive the general equation of motion for the turbulence kinematical vorticity number $\widetilde{\cal V}_{K}$ in turbulent flows of incompressible non-Newtonian fluids, which depicts the underlying dynamical character of $\widetilde{\cal V}_{K}$ and in laminar flows reduces to the general equation of motion for the kinematical vorticity number---the Truesdell number ${\cal V}_{K}$. Second, we obtain an inequality which places the relevant dynamical restriction upon the mean Cauchy stress tensor, the Reynolds stress tensor, and the mean body force density vector in the ensemble-averaged Cauchy equation of motion for turbulence modelling. Moreover, we derive the general Reynolds stress transport equation for turbulence modelling of incompressible non-Newtonian fluids based on Cauchy's laws of motion, which includes as a special case the classical Reynolds stress transport equation for an incompressible Newtonian fluid derived from the Navier--Stokes equation.

Abstract: Stroke causes major long-term disability, with balance impairment significantly affecting quality of life. Personalized prognosis and treatment selection, particularly between TeleRehabilitation (TR) and Conventional Rehabilitation (CR), are crucial. However, current predictive models often lack multimodal integration or tailored recommendations. This paper introduces Causal-MMFNet, a novel deep learning framework. It integrates diverse multimodal time-series data to simultaneously predict balance recovery and allocate individualized treatments in stroke rehabilitation. Key innovations include a dynamic cross-modal attention fusion mechanism, an Individual Treatment Effect (ITE) estimation module for counterfactual outcomes, and causal consistency regularization. Evaluated on the StrokeBalance-Sim dataset, Causal-MMFNet consistently outperforms baselines and state-of-the-art multimodal frameworks, demonstrating superior accuracy and reliability across established metrics. Ablation studies confirm component contributions, while dynamic attention reveals adaptive modality prioritization. The framework's treatment allocation significantly improves patient outcomes, with uncertainty estimation providing clinical confidence. Causal-MMFNet offers a robust, causally-aware solution for personalized decision support in stroke rehabilitation, enhancing patient recovery and optimizing resource allocation.

Abstract: Contactless monitoring of vital signs such as Heart Rate (HR) and Respiratory Rate (RR) has gained significant attention, with vibration-based sensors like geophones showing promise for accurate, non-invasive monitoring. However, most existing systems are developed with healthy subjects and may not generalize well to extreme physiological ranges, such as those observed in infants or patients with arrhythmia. Moreover, the underlying mechanisms of cardiorespiratory vibration dynamics remain insufficiently understood, limiting clinical adoption of these systems. To address these challenges, we present a programmable cardiorespiratory testbed capable of generating realistic HR and RR signals across a wide range (HR: 40–240 bpm, RR: 8–40 bpm). Our system uses a voice coil motor that acts as the vibration source, driven by a Raspberry Pi based control circuit. Unlike similar systems that use separate modules for heart and lung signals, our setup generates both signals using a single motor. The synthetic signals exhibit a strong correlation of 0.85 compared with data from 75 human subjects. We use this system to design signal processing based algorithms for vital signs monitoring and demonstrate their robustness for extreme physiological ranges. The proposed system enhances the understanding of cardiorespiratory vibration dynamics while significantly reducing the time and effort required to collect real-world data.

Abstract: This study develops a multi-source feature-fusion framework that combines transaction histories, mobile-behavior data, credit-bureau information, and merchant-level attributes. The feature space contains over 4,800 engineered variables derived from 3.5 million customer records. A three-stage selection pipeline—correlation filtering, mutual-information ranking, and stability-selection LASSO—reduces dimensionality by 92%. The selected features train a LightGBM model optimized for early-stage (0–30 day) delinquency prediction. The model achieves an ROC-AUC of 0.91 and reduces false-negative early defaults by 37.5% compared with baseline logistic regression. Feature-importance patterns reveal strong interactions between merchant category instability and device-behavior anomalies. The results show the effectiveness of multi-source feature fusion for fine-grained default prediction.

Abstract: Aeropalynology the monitoring and interpretation of airborne pollen has become increasingly relevant in Latin America as allergic rhinitis and asthma rise alongside rapid urbanization, land‑use change, and climate variability. Yet the region’s capacity remains heterogeneous: long‑standing traditions in the Southern Cone coexist with emerging programs in tropical and Andean settings, and many series are not translated into standardized products useful for clinical care or public health. We conducted a structured literature review guided by PRISMA 2020 to synthesize the historical evolution, current monitoring infrastructure, dominant pollen taxa, and translational outputs reported across Latin American countries. Evidence indicates that Mexico currently represents the most mature aeropalynological ecosystem in the region, supported by multi‑site monitoring, open weekly reporting (REMA), multiple city‑level pollen calendars, and emerging computational approaches for pollen identification. Across countries, recurrent high‑impact taxa include Cupressaceae/Juniperus, Fraxinus, Platanus, Olea, Poaceae, Urticaceae, Chenopodiaceae–Amaranthaceae, Rumex, Ambrosia, and Parietaria, with local dominance shaped by biogeography and urban vegetation. Key gaps include limited long‑term continuity outside a few cities, variable methodology (sampler type, taxonomic resolution, units, thresholds), and scarce linkage of pollen exposure metrics with clinical outcomes. Future priorities include harmonized volumetric monitoring, interoperable data standards, routine publication of pollen calendars and thresholds, integration with meteorology for forecasting, and expansion of digital decision‑support tools to improve prevention and management of allergic respiratory diseases in Latin America.

Abstract: Brain cancer metastasis is one of the most common neurological complications associated with various types of cancer, particularly lung cancer, breast cancer, and melanoma. Approximately 20% of cancer patients develop brain metastasis. Current therapeutic strategies are limited and often lack effectiveness, with patient survival typically averaging less than 15 months. As a result, brain metastasis remains one of the leading causes of cancer-related mortality worldwide. Therefore, understanding the mechanisms behind brain metastasis is crucial for improving treatment outcomes. In this review, we provide an overview of the epidemiology, mechanisms, diagnostic approaches, prognostic factors, and treatment strategies associated with brain cancer.

Abstract: Background Patellofemoral pain syndrome (PFPS) is a prevalent overuse injury among recreational cyclists worldwide. Despite its ubiquity, little is known about the lived experiences of people with PFPS, especially in Saudi Arabia, where healthcare and cultural factors may have a special impact on how the condition is managed. The aim of this study is to explore the lived experiences of recreational cyclists with patellofemoral pain syndrome in Al Madinah, Saudi Arabia. Method: A qualitative, descriptive, phenomenological design was employed. Eleven male recreational cyclists aged 28–44 years diagnosed with PFPS were purposely recruited from Al Madinah Physical Therapy Centre. Female participants were excluded due to cultural constraints in sports participation. The participants consented to participate in the study and to be audio recorded. Data were collected using audio-recorded semi-structured interviews using an interview guide. Data were transcribed verbatim and thematically analysed using Atlas.ti version 24. Results: The following themes emerged from our findings: characteristics of patellofemoral pain, functional activities that exacerbate knee pain, psychological and physical effects, coping mechanisms, community and psychosocial constraints, and strategies for managing knee pain. Conclusion: Patellofemoral pain syndrome imposes significant multidimensional burdens on recreational cyclists in Al Madinah, exacerbated by cultural practices. Physiotherapy offers targeted interventions for pain relief, functional restoration, and participation enhancement, underscoring the need for culturally sensitive management programs.