Search Results (1,232)

Extreme drought events are increasingly destabilizing European lowland oak forests, yet within-stand variation in drought legacy effects remains poorly characterized. This study integrates UAV-LiDAR canopy structural analysis with a 68-year dendrochronological record (1952–2019) to examine divergent radial growth responses to the 2012 extreme drought in Turkey oak (Quercus cerris L.) forests of Vojvodina, northern Serbia. LiDAR scanning (Wingtra Gen II, 90 m altitude, spring 2024) enabled objective classification of 180 increment cores from 90 trees across four 5–7 ha experimental plots into two structural zones: a preserved-structure zone (PS; gap fraction ≤ 10%) and a disturbed-structure zone (DS; gap fraction > 10%). Ring width index (RWI) chronologies were developed using the modified negative exponential function and analyzed with linear mixed-effects models (LMMs) incorporating AR(1) temporal autocorrelation. Lloret resilience indices (a reference window of seven years) were computed per individual tree and compared between zones using Mann–Whitney U tests with Bonferroni correction. The key finding is a statistically significant zone × period interaction in all four plots (p = 0.0009–0.033): DS zone trees exhibited a marked post-drought RWI increase (mean +0.22–0.36 units; t-test p < 0.0001 in all plots), while PS zone trees showed no significant post-drought change (p = 0.147–0.258). Pooled Lloret analysis revealed significantly higher recovery (Rt: DS median = 1.693 vs. PS = 1.237; U = 1633, p < 0.0001, r = 0.532) and resilience (Rs: DS = 1.232 vs. PS = 0.932; U = 1574, p < 0.0001, r = 0.482), while resistance (Rc) did not differ between zones (p = 0.569), indicating that DS zone trees were equally susceptible to the drought but recovered far more strongly. The equivalence of Rc between zones critically implies that divergent post-drought trajectories cannot be attributed to differential drought tolerance but instead reflect a structural mechanism operating exclusively in the post-drought period. These results are consistent with a competition release mechanism: drought-induced canopy gap formation in DS zones reduced inter-tree competition for surviving trees, enabling accelerated radial growth recovery. Full article

Dynamic economic growth and climate change increase pressure on water resources, posing a challenge to achieving sustainable development goals, especially in regions with diverse hydrological conditions and development trajectories. This study aims to quantitatively assess the dynamic asymmetry between water management efficiency and the level of sustainable development in the European Union and the Three Seas Initiative (3SI) countries, with particular emphasis on cumulative mechanisms, regional divergence, and the potential low equilibrium trap. The values of the analysed indicators were calculated for 2015, 2021, and 2022, and subsequently their changes were determined for 2021/2015 and 2022/2021. This study was conducted using Eurostat data, applying the CRITIC method for objective weight determination, the TOPSIS technique for constructing synthetic measures, the Kruskal–Wallis and Mann–Whitney tests to assess inter-group differences, and linear regression to identify dependencies. Countries were grouped according to the dynamics of changes in the synthetic water management index. The results indicate a clear asymmetry: the water sector is characterised by a cumulative mechanism and strong divergence (particularly evident in the short period), whereas sustainable development remains significantly more stable, homogeneous, and weakly linearly correlated with previous water achievements. In 3SI countries, a higher rate of improvement in water indicators was observed compared to the rest of the EU; however, no significant synergy with progress in sustainable development was found. The negative impact of the Water Exploitation Index on sustainable development is statistically noticeable but does not confirm the existence of a clear “low equilibrium trap” across the entire 3SI region. This study highlights the need for regionally differentiated, asymmetrical water policies and the integration of water management with broader ecological transformation strategies. Full article

The production and leasing of electric construction machinery play a critical role in the low-carbon transition. However, from a multi-cycle dynamic perspective, there is a lack of targeted research on how to enhance electric goodwill and AI-enabled maintenance service levels while maximizing enterprise profits. To fill this gap, this study incorporates AI-enabled O&M effort, R&D technology, AI-enabled maintenance effort, and advertising effort into a long-term dynamic framework to examine optimal decisions for the manufacturer and the lessor. We assume that the information in the leasing supply chain is symmetric, that the marginal profits of the manufacturer and the lessor are fixed parameters, and that the AI-enabled maintenance service effort level and the electric goodwill are taken as state variables. We develop differential game models across four decision cases: centralized (Case C), decentralized (Case D), unilateral cost-sharing contract (Case U), and bilateral cost-sharing contract (Case B). Results demonstrate monotonic state variable trajectories. Both Case U and Case B can achieve supply chain coordination, with the profit-sharing mechanism in Case B proving superior. In addition, the optimal cost-sharing proportion depends on the relative sizes of the manufacturer’s and the lessor’s marginal profits in both Case U and Case B. The AI-enabled maintenance service plays a significant role in enhancing equipment reliability and supply chain resilience. In addition, the impacts of key parameters on optimal decision variables, state variables, profits, and coordination of the leasing supply chain are comprehensively discussed. Full article

An efficient bacteriophage delivery system needs to be developed to overcome the challenges associated with phage instability, rapid diffusion, and loss of infectivity at the infection site. Hydrogels have been found to be potential carriers. Hydrogels have emerged as promising carriers due to their biocompatibility, tunable physicochemical properties and capacity for controlled release. However, the molecular factors that regulate phage–hydrogel interactions remain poorly understood. In this study, we employed an in silico framework combining molecular docking, molecular dynamics (MD) simulations, MM/PBSA binding energy calculations, machine learning-based adhesion prediction, and diffusion modeling to explore phage–hydrogel interactions at the molecular level. Surface-exposed bacteriophage proteins, such as capsid and tail proteins, were evaluated against eight different hydrogel polymers. Binding site analysis revealed the presence of multiple solvent-accessible pockets that can interact with the polymer. Docking studies showed favorable and stable interactions, with hyaluronic acid showing strong binding affinity to multiple phage proteins (−5.5 to −5.7 kcal/mol) and GelMA showing high affinity to the capsid gp10 protein (−5.6 kcal/mol). The integrity of the structural complexes was further confirmed by 100 ns MD simulations, stable RMSD and RMSF trajectories, compact structural conformations, and favorable MM/PBSA binding energies. Machine learning classification successfully differentiated high- and low-adhesion systems and identified hydrogen bonding and electrostatic interactions as key determinants of sustained yet reversible phage retention. Collectively, our findings suggest that the hydrogels enriched with charged and polar functional groups can facilitate stable but non-destructive phage binding, enabling controlled and sustained release. This study provides mechanistic insights into rational hydrogel design for phage delivery systems and highlights the potential of high-throughput computational strategies to accelerate the development of optimized phage therapeutics. Full article

Meat quality serves as a pivotal determinant of consumer purchasing behavior and of the economic viability of the livestock industry; as such, research into its regulatory mechanisms is of critical significance for the development of modern agriculture. Traditional investigations into meat quality have predominantly centered on sensory and physicochemical assessments of ultimate phenotypic traits, thereby facing inherent limitations in systematically deciphering the intricate molecular regulatory networks underlying meat quality formation. By contrast, an integrated analysis of the transcriptome and metabolome effectively connects the cascade of “gene transcription—metabolic regulation—phenotypic determination,” which has emerged as a core methodological paradigm in contemporary research on the molecular mechanisms governing meat quality. This review systematically delineates the evolutionary trajectory and principal technological frameworks of meat quality evaluation systems, with a focused synthesis of recent advances achieved through combined transcriptomic and metabolomic analyses in the field of meat quality regulation. The scope of this review encompasses core transcriptional regulatory networks associated with meat quality attributes, pivotal metabolic pathways, signal transduction mechanisms, and protein degradation dynamics. Furthermore, the regulatory impacts exerted by genetic variation among breeds, nutritional modulation, rearing environments, and stress responses on meat quality characteristics are comprehensively elucidated. Integrative analysis reveals that combined transcriptome–metabolome approaches transcend the inherent limitations of single-omics investigations, systematically unraveling the hierarchical regulatory mechanisms governing fundamental meat quality traits, such as muscle fiber type differentiation, postmortem glycolytic progression, intramuscular fat deposition, and flavor compound accumulation. Such integrative strategies have facilitated the identification of functional genes and metabolic biomarkers with potential utility for the early prediction of meat quality outcomes. Concurrently, this review acknowledges persistent challenges confronting the field, including the absence of standardized protocols for multi-omics data integration, insufficient functional causal validation, and a discernible disconnect between research discoveries and practical industrial implementation. Building upon this comprehensive assessment, prospective directions for future multi-omics research in meat quality are proposed, accompanied by the formulation of an integrated end-to-end improvement framework spanning fundamental research, technological innovation, and industrial application. Collectively, this review provides a systematic theoretical foundation for the in-depth elucidation of mechanisms that determine meat quality and the precision-oriented regulation of quality-determining traits in livestock production practices, thereby offering substantial scientific guidance for quality improvement initiatives within the animal husbandry sector. Full article

Background/Objectives: Patients with breast cancer show substantial heterogeneity in terms of psychological adjustment following diagnosis. We aimed to characterize longitudinal trajectories of quality of life (QoL) and depressive symptoms during the first 18 months post-diagnosis and to identify robust clinical, psychosocial, and behavioral predictors associated with distinct adjustment pathways. Methods: Women (N = 538; mean age 55.4 years; range 40–70) with operable breast cancer (stages I–III) were drawn from the multicenter BOUNCE cohort. QoL (Global Health Status/QoL scale of the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30) and depressive symptoms (depression subscale of the Hospital Anxiety and Depression Scale) were assessed at baseline and months 3, 6, 9, 12, 15 and 18. Latent class growth analysis and growth mixture modeling identified distinct trajectory classes. Associations between early predictors and trajectory membership were examined using logistic regression combined with elastic net regularization. Results: Depression trajectories demonstrated heterogeneity, with groups characterized by persistent resilience (59.7%), stable moderate/high (25.3%), delayed onset (5.0%), and recovery (10.0%). QoL trajectories ranged from stable excellent (13.2%) and stable high (40.7%) to moderate (31.4%) and persistent low/deteriorating (6.9%), as well as a distinct recovering trajectory (7.8%). Trajectory differentiation was primarily driven by psychological resources, symptom burden, functional status, and coping processes, alongside specific contributions from clinical factors. Conclusions: Distinct subgroups of women with breast cancer follow divergent adjustment pathways. These findings highlight the multidimensional nature of resilience and support the need for tailored interventions that promote long-term well-being beyond simple risk reduction. Full article

Background: A major technical challenge in single-cell transcriptomics is the absence of an integrative analytic pipeline that can simultaneously leverage gene regulatory network (GRN) architecture, AI-assisted gene panel discovery, and functional relevance analyses to generate coherent biological insights. Existing approaches often treat these components independently, focusing on clusters, marker genes, or predictive features without integrating them into a mechanistically grounded framework. Consequently, comprehensive screening that links regulatory association, gene signature screening, and functional interpretation within single-cell datasets remains limited, underscoring the need for an integrated strategy. Methods: We developed an integrative bioinformatics pipeline based on Gene regulatory network–AI–Functional Analysis (GAFA), combining latent-space integration, unsupervised clustering, diffusion pseudotime analysis, lineage-resolved generalized additive modeling, GRN inference, and machine learning-based gene panel discovery. This framework enables systematic mapping of cell-state structure, reconstruction of differentiation and effector trajectories, and identification of transcriptional and regulatory features strongly associated with clinical outcomes. As a case study, we applied the pipeline to NK cell transcriptomes from six CML patients (two early relapse, two late relapse, two durable treatment-free remission—TFR; 15 samples) collected at TKI discontinuation and 6–12 months after therapy cessation. Results: We reanalyzed publicly available scRNA-seq data from a previously published CML cohort to evaluate NK-cell transcriptional programs associated with treatment-free remission and relapse. We resolved six transcriptionally distinct NK cell states spanning CD56^bright-like cytokine-responsive, early activated, terminally mature, cytotoxic, lymphoid trafficking, and HLA-DR⁺ immunoregulatory populations, each exhibiting outcome-specific compositional differences. Pseudotime analysis revealed two major NK cell lineages—a maturation trajectory and a cytotoxic effector trajectory. TFR samples displayed balanced occupancy of both lineages, whereas early relapse samples showed marked depletion of the maturation branch and preferential accumulation in cytotoxic end states. AI-guided feature selection and random forest modeling identified an 18-gene panel that distinguished NK cells from TFR and relapse samples in an exploratory manner. Among them, CST7, FCER1G, GNLY, GZMA, and HLA-C were conventional NK-associated genes, whereas ACTB, CYBA, IFITM2, IFITM3, LYZ, MALAT1, MT2A, MYOM2, NFKBIA, PIM1, S100A8, S100B, and TSC22D3 were novel. The GRN inference further uncovered outcome-specific regulatory modules, with RUNX3, EOMES, ELK4, and REL regulons enriched in TFR, whereas FOSL2 and MAF regulons were enriched in relapse, and their downstream targets linked to IFN-γ signaling, metabolic reprogramming, and immunoregulatory feedback circuits. Conclusions: This AI-enabled single-cell analysis demonstrates how NK cell state composition, differentiation trajectories, and regulatory network rewiring collectively shape TFR versus relapse following TKI discontinuation in CML. The integrative pipeline provides a modular framework that could be extended to additional datasets for data-driven biomarker discovery and mechanistic stratification, and highlights candidate transcriptional regulators and NK cell programs that may be leveraged to improve remission durability, pending validation in larger patient cohorts. Full article

To ensure reliable lane change behavior in-wheel motor-driven electric vehicles (IWM-EVs) under steer-by-wire (SBW) failure, this paper presents an integrated lateral–longitudinal lane change control strategy based on differential steering. The control framework and relevant models are first established. An upper-layer model predictive control (MPC) controller is then designed to simultaneously achieve lateral path tracking and longitudinal speed regulation, outputting the desired front-wheel steering angle and acceleration. Finally, a model-free adaptive control (MFAC)-based lower-layer lateral controller transforms the desired steering angle into differential driving torques for the front wheels, while a feedforward–feedback lower-layer longitudinal controller (incorporating drive/brake switching and PI control) computes the required driving torque or braking pressure. Co-simulation in Matlab/Simulink R2022b and CarSim R2020 reveals that the MPC controller designed in this study outperforms the LQR-PID controller, reducing the maximum absolute values of lateral error, heading error, front-wheel steering angle, yaw rate and sideslip angle by 42.9%, 50.0%, 7.8%, 2.8% and 10.3%. The proposed hierarchical control strategy outperforms the compared hierarchical controller, reducing the maximum absolute values of the lateral displacement error, heading error and yaw rate by 17.9%, 6.7%, and 33.3%. These results verify that the strategy can improve trajectory tracking accuracy and achieve basic differential steering functionality in specific scenarios. Full article

This study examines the determinants of tourism resilience and recovery following global crises using a comparative cross-country approach. A composite Tourism Resilience Index (TRI) is constructed based on post-crisis recovery in tourism employment, tourism GDP and international arrivals, and its determinants are analyzed through descriptive, correlational and exploratory multivariate regression analysis. The results reveal significant heterogeneity in resilience trajectories across countries, indicating that income level alone does not explain recovery patterns. Institutional and structural factors, including the degree of economic liberalization and market composition, play a critical role in shaping post-crisis tourism performance. These findings contribute to the literature on tourism resilience by providing empirical evidence with policy implications for improving adaptive capacity in tourism-dependent economies. Furthermore, the results highlight the multidimensional nature of tourism resilience and provide evidence-based insights for the design of differentiated policy strategies aimed at strengthening the sector’s capacity to withstand future global crises. Full article

Understanding why cities diverge in motor vehicle ownership trajectories is critical for designing differentiated and sustainable transport policies. This study develops an integrated national–city analytical framework to examine heterogeneous urban motorization processes in China. A national Gompertz curve is first estimated to represent the benchmark income–ownership relationship. City-specific deviations are then decomposed into two interpretable dimensions: a horizontal stage parameter (h), capturing relative advancement or delay in motorization timing, and a vertical scaling parameter (s), reflecting persistent ownership intensity differences conditional on income. Results show substantial and multi-dimensional heterogeneity across cities. Stage timing (h) and ownership intensity (s) are only weakly correlated, indicating that earlier transition into higher motorization stages does not necessarily imply above-benchmark ownership intensity. Random forest models with time-forward validation demonstrate strong explanatory power (R² ≈ 0.88 for h and 0.80 for s). SHAP-based interpretation reveals that stage deviation is primarily associated with transport supply and urban structural characteristics, whereas ownership intensity deviation is more strongly linked to urban spatial scale and economic structure. Regulatory measures, including purchase and driving restrictions, exhibit comparatively smaller and heterogeneous effects. By disentangling timing and intensity dimensions of urban motorization, this study refines conventional income-based diffusion models and provides quantitative evidence that structural urban characteristics play a more fundamental role than regulatory interventions in shaping inter-city motorization differences. Full article

This study develops and evaluates three complementary predictive modeling frameworks for gearbox oil temperature forecasting: Stochastic Differential Equation (SDE) modeling with iterative Markov correction, multi-objective genetic algorithm-enhanced grey modeling (MOGA-GM(1,N)), and multi-output Gaussian Process Regression (MO-GPR). The study used supervisory control and data acquisition (SCADA) data from a 1.5 MW wind turbine gearbox, comprising 14 temperature measurements spanning 789 operational hours. The SDE framework partitions temperature evolution into deterministic aging effects and stochastic environmental perturbations, achieving a fitting accuracy of 2.5% and testing accuracy of 8.0% after thirty iterative corrections. The MOGA-GM(1,N) approach optimizes weight coefficients through the dual objective of minimizing the posterior difference ratio and maximizing small error probability, attaining first-class accuracy classification ($C=0.06$; $P=0.99$) while identifying mechanical loads and rotational speeds as dominant thermal drivers. MO-GPR demonstrates competitive performance with uncertainty quantification capabilities, achieving RMSE values of 2.51–7.48 depending on training SCADA data proportions. Comparative analysis shows that the iteratively refined SDE methodachieves the best prediction accuracy in this case study for continuous thermal trajectory forecasting, while MOGA-GM(1,N) excels at wear source diagnostics and operational factor analysis. The proposed framework addresses persistent challenges in wind turbine condition monitoring, including extreme nonlinearity, discontinuous data, and unpredictable thermal spikes. The results suggest potential for implementation in preventive maintenance systems, enabling timely intervention before critical thermal thresholds that precipitate component failure. Full article

Non-communicable diseases (NCDs) are the leading causes of mortality in Serbia, with cardiovascular diseases (CVDs) accounting for a substantial share of premature mortality. In alignment with Sustainable Development Goal (SDG) Target 3.4, which aims to reduce premature mortality from NCD by one-third by 2030 relative to 2015, this study forecasts changes in CVD mortality counts at the municipal level in Serbia. Time-series data for the period 2005–2022 were analyzed within a spatio-temporal forecasting framework implemented in the Space Time Pattern Mining toolbox in ArcGIS Pro (Version 3.1). Three established forecasting models (Curve Fit Forecast, Exponential Smoothing, and Forest-based) were applied, and the most accurate model for each municipality was selected using location-specific municipality-level validation. The results reveal pronounced spatial variation: approximately half of the municipalities (51.2%) are forecasted to experience a decline in CVD mortality counts by 2030, while others are expected to show increases or no statistically significant change. Forecasted differences range from a 15.1% decrease to a 13.9% increase across municipalities, indicating heterogeneous spatial trajectories and suggesting that achieving SDG Target 3.4 may remain challenging without targeted interventions across municipalities where mortality reductions are not forecasted. Although the study does not introduce new forecasting methods, it provides a novel spatially disaggregated application of multi-model forecasting to support municipality-level monitoring of SDG 3.4. The results underscore the need for geographically differentiated public health policies and demonstrate the value of spatial forecasting approaches for supporting equitable and targeted health planning. Full article

Rapid urbanization has promoted socioeconomic growth but has exacerbated spatial-structure imbalances. This study investigates 41 prefecture-level cities in the Yangtze River Delta (YRD) from 2010 to 2022. Using nighttime light data, we compute the Comprehensive Nighttime Light Index (CNLI) to track urbanization dynamics and delineate built-up areas. Furthermore, we apply random-forest recursive feature elimination with cross-validation (RF-RFECV) and a Shapley additive explanations (SHAP)-based interpretation framework to quantify the spatiotemporal evolution of urbanization drivers. The results indicate that urbanization in the YRD increased steadily overall during the study period. Shanghai maintained its core leadership, Jiangsu and Zhejiang advanced steadily, and Anhui rapidly caught up driven by regional integration policies. Although regional disparities generally converged, persistent absolute gaps in small and medium-sized cities and inland areas remain a prominent challenge to balanced development. Spatially, urbanization exhibits a gradient differentiation of “higher in the east and lower in the west, and higher along rivers and coasts than inland.” The regional spatial structure gradually shifted from an early “pole-core–belt” pattern to a polycentric and networked urban agglomeration system, with metropolitan areas and economic belts serving as important carriers for promoting spatial balance. Furthermore, built-up areas exhibit a trajectory of “core agglomeration, corridor-oriented expansion, and intensive transition.” The shrinking coverage of the standard deviational ellipse and a slowdown in expansion rates suggest a shift from extensive outward sprawl to more concentrated development. Regarding driving mechanisms, YRD urbanization has evolved from early-stage factor-scale expansion to a later-stage efficiency- and innovation-driven trajectory. While population density remained the dominant driver, early-stage reliance on transport infrastructure and fiscal decentralization was largely replaced by the strengthening effects of per capita output and green innovation. Overall, these findings provide empirical evidence for optimizing spatial patterns and designing differentiated policies for high-quality urbanization in the YRD. Full article

Chronic lung allograft dysfunction (CLAD) remains the principal limitation to long-term survival after lung transplantation (LT). Early molecular alterations within the graft may precede clinically overt functional decline, but their biological significance remains incompletely defined. In this single-center exploratory pilot study, 16 bilateral lung transplant recipients underwent bronchoalveolar lavage (BAL) sampling at 7 days, 15 days, and 3 months post-transplantation. BAL-derived microRNA (miRNA) profiles were analyzed longitudinally and correlated with long-term clinical outcomes, including CLAD development and phenotypic classification into bronchiolitis obliterans syndrome (BOS) or restrictive allograft syndrome (RAS), over extended follow-up (mean 98 months). Distinct early miRNA signatures were detectable within the first weeks after transplantation and were associated with divergent long-term clinical trajectories. Specific miRNAs, namely let-7e-5p and miR-30d-3p, were associated with subsequent CLAD, whereas differential expression patterns distinguished trajectories toward BOS or RAS. Enrichment analyses highlighted networks related to innate immune activation, hypoxia, tissue remodeling, and PI3K–mTOR signaling. Notably, the occurrence of acute rejection did not differ significantly between patients who developed CLAD and those who remained stable. These findings, although preliminary, suggest that early BAL-derived miRNA profiles may reflect biologically distinct graft states associated with long-term CLAD phenotypes. Full article

Objectives: The long-term effects of rheumatoid arthritis (RA) therapies on bone mineral density (BMD) remain incompletely characterized. We aimed to evaluate BMD trajectories over an 8-year follow-up in patients with RA treated with conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) or biological DMARDs (bDMARDs) in real-world practice. Methods: Patients were selected from an observational RA cohort established at Nice University Hospital between 2001 and 2016. Participants were classified into two groups according to treatment regimen (csDMARD only or any bDMARD exposure). BMD was assessed by dual-energy X-ray absorptiometry at baseline and after 1, 2, 3, 5, and 8 years at the lumbar spine, femoral neck, and total hip. Longitudinal changes in BMD were analyzed using multivariable linear mixed-effects models adjusted for age, sex, body mass index (BMI), disease duration, seropositivity, glucocorticoid use, anti-osteoporosis treatment, and clinical response. Results: A total of 312 patients were included, of whom 181 received bDMARDs and 131 were treated exclusively with csDMARDs. BMD showed limited change during the first two years in both groups. Beyond two years, modest declines were observed at hip sites at subsequent time points, whereas lumbar spine BMD did not demonstrate significant longitudinal change in pointwise analyses. In mixed-effects models, the treatment group–time interaction was significant for lumbar spine (p = 0.004) and total hip (p = 0.04), but not for the femoral neck (p = 0.34), indicating differential BMD trajectories over time between treatment groups. In the csDMARD group, lumbar spine and total hip BMD decreased by a mean of 0.0006 and 0.0005 g/cm² per month, respectively, whereas no significant slopes were observed in the bDMARD group. Older age was associated with lower BMD, while male sex and higher BMI were associated with higher BMD across sites. Conclusions: In this long-term real-world cohort, BMD remained relatively stable during the first two years of follow-up. Longitudinal analyses suggested a less pronounced decline in lumbar spine and total hip BMD trajectories among bDMARD-treated patients compared with those receiving csDMARD alone, underscoring the need for ongoing bone health monitoring in RA. Full article