Search Results (165)

Although the European Union positions the bioeconomy at the core of its sustainability transition and the European Green Deal, the cross-country distribution of bioeconomy value-added associated with mission-oriented public R&D support remains highly uneven. This paper investigates how national researcher capacity (as a proxy of absorptive capacity) shapes the macroeconomic effectiveness of bioeconomy-oriented public R&D support, and how societal climate-oriented environmental concern acts as a direct structural driver of bioeconomy value-added. Using a panel dataset of 27 EU Member States from 2008 to 2020, the study constructs an original bioeconomy-specific measure of government budget appropriations for R&D (GBARD) and estimates two-way fixed-effects models with Driscoll–Kraay standard errors to account for cross-sectional dependence. The findings reveal a clear capacity-dependent conditional moderation effect: public R&D support is significantly associated with higher bioeconomy value-added only when a critical mass of researcher capacity is present. Sectoral disaggregation demonstrates that business enterprise researcher capacity acts as the primary transmission channel linking public funds to the market, whereas higher-education capacity shows no statistically significant short-to-medium-term moderating effect, consistent with the academic research commercialisation time lags documented in the literature. Additionally, societal climate-oriented environmental concern is positively associated with bioeconomy value-added in the baseline models, consistent with its role as a demand-side factor fostering receptive conditions for bio-based transitions. The study concludes that increasing mission-oriented R&D funding alone is likely insufficient; to successfully cross the “valley of death,” public R&D should be accompanied by complementary policies that build private-sector absorptive capacity and cultivate green market demand. Full article

Background and Objectives: The SARS-CoV-2 pandemic disrupted oral and maxillofacial surgery (OMS) services worldwide because of the high aerosol-generating nature of head-and-neck procedures, restricted access to elective dental care, and systemic reallocation of hospital resources. Continuous longitudinal multi-year data covering both the pandemic and the post-pandemic phases from regional Romanian (and more broadly central and southeastern European) emergency centers remain scarce. We aimed to quantify the impact of the pandemic on OMS activity in a large Romanian regional referral center and to evaluate post-pandemic resilience. Materials and Methods: We conducted a retrospective single-center study of all inpatient admissions to the OMS Clinic of a tertiary emergency hospital in western Romania between 1 January 2018 and 31 December 2024. Three periods were pre-specified: pre-pandemic (2018–2019), pandemic (2020–2022) and post-pandemic (2023–2024). A Newey–West segmented interrupted-time-series (ITS) regression and a negative-binomial monthly count model with Fourier seasonality were fitted; length of hospital stay was further analyzed with a multivariable gamma-log generalized linear model adjusted for age, sex, county, primary ICD-10 chapter and total ICD-10 codes. Variables analyzed included case volume, demographics, primary and secondary ICD-10 diagnoses, length of hospital stay (LOS), case complexity (total ICD-10 codes per admission) and in-hospital mortality. Results: A total of 11,628 inpatient admissions corresponding to 8084 unique patients (56.5% male; mean age 52.2 ± 19.2 years) were analyzed. Compared with the pre-pandemic baseline (mean 2037 admissions/year), annual volume dropped by 45.1% in 2020, 44.0% in 2021 and 32.3% in 2022, with a nadir of −76% during the first state of emergency (April 2020; n = 34 admissions). Recovery was rapid; 2024 exceeded the pre-pandemic baseline by +10.1% on raw counts and by +16.2% on admissions per 100,000 catchment population using year-specific INS denominators. The segmented ITS regression confirmed an immediate level drop of −114.2 admissions/month in March 2020 (95% CI −133.1 to −95.3; p < 0.001) and a positive post-intervention slope of +2.06 admissions/month (95% CI 1.23–2.88; p < 0.001), with observed monthly volume returning to the counterfactual projection by October 2023. The case mix shifted significantly (χ² = 406.9, p < 0.0001); elective benign neoplasm admissions were reduced from 7.2% to 2.0%, while neoplasms of uncertain behavior nearly doubled from 15.7% to 27.5%. Case complexity increased during the pandemic (mean ICD codes 4.08 ± 2.42 vs. 3.44 ± 2.30; p < 0.001); after exclusion of administrative codes (whole Z chapter and U07.x), the difference attenuated to 3.34 vs. 3.17 codes (still p < 0.001 by Kruskal–Wallis), indicating that the largest portion of the unadjusted increase was driven by the new mandatory pre-admission SARS-CoV-2 screening code Z11.5 rather than true clinical complexity. Notably, the clinically interpretable proxy R63.3 (feeding difficulty) independently rose from 41.5% to 53.1%. The crude median LOS did not differ between the pre-pandemic and pandemic periods (3.07 vs. 3.06 d; p = 0.19) and dropped significantly post-pandemic (2.22 d; p < 0.001); however, after multivariable adjustment for case mix, age, sex, county and code count, the LOS was 15.7% shorter during the pandemic (adjusted ratio 0.84, 95% CI 0.82–0.87; p < 0.001) and 22.8% shorter post-pandemic (adjusted ratio 0.77, 95% CI 0.75–0.80; p < 0.001) relative to baseline. Conclusions: The pandemic caused a severe but transient contraction of OMS activity accompanied by increased case complexity and a marked shift away from elective surgery. Inpatient volume returned to and exceeded the pre-pandemic baseline by 2024. These results support the value of standing pandemic-preparedness protocols, sustained access to preventive dental care, and integrated tele-triage pathways for future public-health crises. Full article

This article examines how smart cities can act as catalysts for enterprise development by integrating technological, infrastructural, governance and human capital dimensions into a coherent urban innovation ecosystem. Drawing on an extensive literature review, the study first conceptualizes smart cities as adaptive systems that combine physical infrastructure, digital data layers, and institutional frameworks, creating conditions for knowledge spillovers, entrepreneurial opportunities, and business model innovation. Empirically, the research is based on an expert survey conducted among 54 specialists from academia, business, and public administration, who assessed the importance of technological, infrastructural, governance, innovation ecosystem, and human capital factors for enterprise development in the context of smart cities. The results suggest that advanced digital technologies, smart infrastructure, open data, R&D support, startup programs and talent development are perceived by experts as key, mutually complementary drivers of firms’ innovation, efficiency, sustainable growth, and competitiveness, with notable differences between expert groups. On this basis, the study proposes a synthetic model of relationships and impact pathways linking smart city components with enterprise outcomes. The paper concludes with a discussion of the study’s limitations, related to the expert-based, country-specific, and perceptional character of the data, and outlines directions for further quantitative and qualitative research on the firm-level effects of smart city development. Full article

In Europe, travel-associated Legionnaires’ disease (TALD) cases require timely environmental investigations to support risk assessment, rapid control measures, and prompt reporting of investigation findings to the European Legionnaires’ Disease Surveillance Network (ELDSNet). This study evaluated TALD-related environmental investigations conducted during 2025 and early 2026 in Crete, Greece, following notifications through ELDSNet. Overall, 30 notifications corresponded to 24 unique confirmed TALD cases with illness onset in 2025 and 24 implicated hotels, with some cases involving stays in multiple hotels and Regional Units and clusters identified in some implicated hotels. The investigation framework combined microbiological, physicochemical, and operational data, focusing on delays from symptom onset, notification, sampling, and laboratory reporting. Overall, 516 environmental samples were collected, of which 503 yielded valid analytical results. Among the 503 samples analyzed, Legionella spp. were detected at ≥50 colony-forming units per liter (CFU/L) in 127 samples (25.25%). This included 123 samples positive for L. pneumophila (24.45%), of which 31 were serogroup 1 (6.16%). Concentrations exceeding the 1000 CFU/L threshold were recorded in 53 samples (10.54%). Operational indicators varied, with median values of 31.0 days for reporting delay (RD), 14.5 days from notification to first sampling (TTF), 47.5 days from symptom onset to first sampling (TDS), and 67.0 days from symptom onset to first laboratory result (OELR). The findings underscore the necessity to document response delays, enhance inspector capacity and cross-regional coordination, and integrate microbiological results with operational indicators. This integration is crucial for facilitating earlier environmental risk assessments, expediting reporting, and implementing more effective TALD public health interventions. Full article

Hybrid brain–computer interfaces (BCIs) have attracted growing research attention because they combine the millisecond-level temporal resolution of electroencephalography (EEG) with the spatially informative hemodynamic responses of functional near-infrared spectroscopy (fNIRS). However, most existing deep fusion methods rely on static late-fusion strategies, which tend to underexploit latent cross-modal dependencies and are vulnerable to modality-specific signal degradation. To address these limitations, we propose MGFNet, a multi-granularity fusion network for hybrid BCI decoding. MGFNet contains three components: (1) intra-modal encoders that learn modality-specific spatiotemporal representations from EEG, oxygenated hemoglobin (HbO), and deoxygenated hemoglobin (HbR) signals; (2) cross-modal interaction encoders that temporally align paired modalities and use dilated convolutions to capture long-range EEG-fNIRS dependencies; and (3) a Coupling-Guided Sparse Component Routing (CGSCR) module that estimates sample-specific cross-modal coupling and performs adaptive discrete routing. We further introduce a deep supervision strategy to stabilize optimization and improve branch-level discriminability. Under a within-subject held-out evaluation protocol on a public benchmark dataset, MGFNet achieved classification accuracies of 99.40% on the n-back task and 99.03% on the word generation (WG) task, outperforming representative comparison methods evaluated under a matched protocol. Ablation studies further confirmed the contributions of the intra-modal encoders, the cross-modal interaction encoders, and the CGSCR module. Under controlled EEG corruption with additive white Gaussian noise at −10 dB, MGFNet outperformed a static-fusion variant by 9.23 percentage points on the n-back task and 6.31 percentage points on the WG task. These results support the effectiveness of MGFNet in the present offline within-subject setting and indicate improved robustness under controlled single-modality degradation. Full article

Macrococcus is a genus of Gram-positive cocci in the Staphylococcaceae family and a close phylogenetic relative of Staphylococcus. It is not a significant human pathogen but is known to widely colonize different environments, including animal skin and food products. Phylogenetically, Macrococcus is distinct from yet closely related to Staphylococcus, particularly the sciuri group. The species is effectively identified through such molecular markers as hsp60 and 16S rDNA. A key biochemical feature is an identified FAD-dependent oleate hydratase in Macrococcus equipercicus (M. equipercicus). Critically, Macrococcus carries various mobile antibiotic-resistance genes, especially against β-lactams (e.g., mecB, mecD) and macrolides (e.g., mef(F), msr(G)); these genes are located on plasmids, SCCmec-like elements, or resistance islands (e.g., McRI_mecD), which facilitates their horizontal transfer. Surveillance confirms the widespread presence of methicillin-resistant Macrococcus, often with a multidrug-resistant phenotype, in food animals and their products. Although its own pathogenicity is low, Macrococcus acts as a reservoir and transmission platform for resistance genes: through horizontal gene transfer, it can potentially confer resistance to pathogenic Staphylococcus, thereby posing a threat to animal and public health. This review summarizes the basic biological characteristics and drug resistance-related research progress of the genus Macrococcus; it aims to provide a reference for subsequent studies as well as to establish technical support and a theoretical basis for the epidemiological investigation, drug-resistant strain identification, and clinical drug-resistance risk prevention and control of Macrococcus. Full article

The hypothesis that Group A beta-haemolytic Streptococcus (GAS) triggers an autoimmune cascade targeting basal ganglia dopaminergic circuits—producing obsessive–compulsive disorder (OCD), tic disorders, or chorea depending on the receptor subtype involved—is biologically compelling and supported by emerging molecular evidence. Yet PANDAS has remained a diagnostic conundrum since its original description in 1998, with ongoing uncertainty surrounding diagnostic criteria, the interpretation of streptococcal serology, and the distinction from primary neurodevelopmental disorders. This study aimed to review the diagnostic challenges of PANDAS, with focus on streptococcal serology interpretation, advances in dopamine receptor autoantibody biology, the genetic epidemiology of primary tic disorders, and the differential diagnosis of acute neuropsychiatric presentations in children. A structured narrative review was conducted using PubMed, MEDLINE, EMBASE, and the Cochrane Library for publications from 1998 to early 2025 addressing PANDAS, PANS, streptococcal antibodies, childhood movement disorders, autoimmune encephalitis, and the genetics of tic disorders. No currently available biomarker—including ASO, anti-DNase B, anti-basal-ganglia antibodies, or the Cunningham Panel—has demonstrated adequate individual-level diagnostic accuracy for PANDAS. Emerging molecular evidence identifies anti-D1R autoantibodies, acting via G protein-and beta-arrestin-mediated signalling, as candidate biomarkers for PANDAS/PANS neuropsychiatric phenotypes, and anti-D2R autoantibodies for Sydenham chorea movement phenotypes; independent replication in unselected populations is required. Primary tic disorders carry heritability estimates of 50–80% and first-degree familial risk ratios of approximately 18-fold in large population-based cohorts. Prospective blinded studies have not demonstrated a consistent population-level association between GAS infections and tic or OCD exacerbations: PANDAS and PANS remain diagnoses of exclusion. The high background prevalence of both GAS exposure and primary neurodevelopmental disorders in overlapping paediatric age ranges creates conditions for incidental temporal co-occurrence. In the absence of validated molecular biomarkers, diagnostic imprecision carries direct clinical consequences: children may be exposed to treatments with significant risk profiles—including IVIG, plasma exchange, and prolonged antibiotic prophylaxis—while evidence-based therapies are delayed. A stepwise diagnostic approach incorporating the full differential diagnosis is both an epistemological and a patient safety imperative. Full article

Sanfilippo syndrome type B, also known as mucopolysaccharidosis type IIIB (MPS IIIB), is a rare autosomal recessive lysosomal storage disorder caused by mutations in the N-acetyl-α-D-glucosaminidase (NAGLU) gene, which encodes the enzyme α-N-acetylglucosaminidase. This enzyme is essential for degrading heparan sulfate. The deficiency leads to toxic accumulation within cells. To investigate the impact of NAGLU mutations, mutational data were retrieved from public databases including NCBI, UniProt, and HGMD. A total of 162 variants were evaluated using sequence-based prediction tools to identify deleterious mutations, followed by structure-based in silico analyses to assess changes in protein stability, biophysical properties, and ligand-binding potential. Among the analyzed mutations, the I154R variant was identified as the most deleterious, showing disease-associated characteristics, structural instability, and impaired functional properties. Molecular docking with N-acetylglucosamine (NAG) revealed binding affinities of −4.17 kcal/mol for the native protein and −3.97 kcal/mol for the I154R mutant, suggesting a retained yet slightly reduced binding potential. Molecular dynamics simulations supported these findings, indicating stable trajectories, favorable interaction profiles, and moderate flexibility for both complexes. These results enhance our understanding of NAGLU-related pathogenicity in MPS IIIB, contributing to improved health care strategies and offering a valuable foundation for future therapeutic developments targeting enzyme dysfunction in Sanfilippo syndrome type B. Full article

Background/Objectives: Microbial resistance to antibiotics has become a major global public health problem, threatening the effectiveness of current therapeutic strategies. The present study seeks to investigate natural compounds originating from fungal sources for their ability to interfere with efflux pump-mediated resistance in multidrug-resistant (MDR) bacteria, with the overarching goal of uncovering new candidates for antimicrobial therapeutic development. A chemical investigation of the ethanol extract of Termitomyces clypeatus was carried out to isolate and identify its constituents. Methods: Structural elucidation of the isolated metabolites was achieved through 1D and 2D NMR spectroscopy supported by mass spectrometric data. The crude extract and the purified compounds were then evaluated for their antibacterial activities individually, in the presence of an efflux pump inhibitor, and in combination with three antibiotics, using standardized microdilution assays. Results: Chromatographic separation of the extract yielded eleven known compounds, including three sphingolipids: (9Z,12Z)-N-(1,3,4-trihydroxyoctadecan-2-yl)octadeca-9,12-dienamide (1), 2-hydroxy-N-(1,3,4-trihydroxyoctadecan-2-yl)hexadecanamide (2), and cerebroside B (3); four steroids: ergosterol (4), cerevisterol (5), ergosterol peroxide (6), and 5α,6α-epoxy-(22E,24R)-ergosta-8(14),22-diene-3β,7α-diol (7); one alkaloid: piperine (8); one carbohydrate: D-mannitol (9); and two phthalates: dimethyl phthalate (10) and bis(2-ethylhexyl) terephthalate (11). GC–MS analysis led to the identification of eight fatty acid derivatives (12–19). Sub-fraction A, along with compounds 3, 4, and 8, exhibited moderate antibacterial activity against some tested strains, with MIC values of 64 μg/mL. These compounds were identified as substrates of bacterial efflux pumps, and their presence enhanced the antibacterial effects of ciprofloxacin, doxycycline, and amikacin. Conclusions: The findings of the present work indicate that Termitomyces clypeatus contains compounds with potential therapeutic value, as adjuvants that enhance the activity of conventional antibiotics. Full article

The commissioning of private suppliers to conduct technological research and development (R&D) has become a central instrument for the public sector to promote technological innovation in public goods. However, information asymmetry and goal divergence between public principals and suppliers create moral hazard problems that can undermine innovation efficiency. Purely output-based incentive contracts are often insufficient to curb suppliers’ opportunistic behavior, especially when R&D outputs are uncertain and difficult to measure ex ante. This raises the need to complement incentive contracts with supervision mechanisms and to jointly optimize the structure of incentives and monitoring efforts. Building on principal–agent theory, this paper develops an incentive model that explicitly incorporates both supervision intensity and regulatory difficulty and analyzes how these factors shape suppliers’ R&D efforts and the public sector’s benefit levels. The results show, first, that appropriately designed supervision can increase suppliers’ willingness to invest in R&D and thereby help to strengthen the effectiveness of incentives. Second, incentive contracts need to be adjusted in line with supervision intensity: by reallocating rewards based on both observed outputs and supervision results, the public principal can induce higher effort levels. Third, as regulatory difficulty rises, the marginal effectiveness of supervision changes; under high regulatory difficulty, excessive supervision may even weaken incentive effects, implying that supervision intensity should be kept within a moderate range. Fourth, there exists an interior level of supervision intensity that balances monitoring costs against incentive benefits and thus maximizes the principal’s overall expected payoff. Viewed from a system engineering perspective, commissioned public goods R&D constitutes a complex multi-actor system subject to external disturbances in which incentive and supervision mechanisms operate as joint control structures regulating system behavior. The findings provide analytical support and policy-relevant insights for designing and calibrating supervision and incentive mechanisms in technological innovation projects for public goods. Full article

Background: Upper respiratory tract infections (URTIs), allergic rhinitis (AR), and chronic rhinosinusitis (CRS) are prevalent and burdensome inflammatory disorders. Probiotics may modulate immune responses via gut–respiratory axis signaling, but their clinical efficacy across these conditions remains uncertain and highly heterogeneous. Methods: We conducted a PRISMA-guided systematic review and random-effects meta-analysis of randomized controlled trials (RCTs) evaluating oral or topical probiotics for URTIs, AR, or CRS (MEDLINE, EMBASE, CENTRAL, and Web of Science; inception to July 2025). Disease severity category (acute, subacute, chronic), episode incidence, and duration of illness were extracted alongside symptom scores. Risk of bias was assessed using the Cochrane RoB 2 tool, and certainty of evidence was graded using the GRADE framework. Results: Thirty-two RCTs were included. In URTIs, certain strains [e.g., Lactiplantibacillus plantarum DR7, Lactobacillus rhamnosus GG] reduced symptom duration and antibiotic use; however, the pooled incidence reduction was non-significant (RD = −0.07; 95% CI: −0.23 to 0.09; p = 0.38), with very high heterogeneity (I² = 93.12%), limiting interpretability. In AR, probiotics reduced TNSS and improved quality of life (SMDs −0.72 to −2.30) in individual trials supported by immune marker changes [e.g., increased IL-10, decreased IgE]. In CRS, only two small trials—differing in delivery route (topical vs. oral), CRS phenotype, and publication era (2009 and 2017)—with conflicting effect directions were identified; formal meta-analysis was not performed given insufficient and methodologically heterogeneous data, and CRS findings are reported descriptively only. GRADE certainty ranged from very low (URTI incidence) to low (AR symptoms, URTI illness burden). Conclusions: Probiotic effects appear strain- and condition-specific. URTI pooled incidence data are unreliable due to extreme heterogeneity; individual strains show consistent benefits on illness burden and AR symptoms/quality of life. Evidence for CRS is insufficient for meta-analytic conclusions; findings are reported descriptively pending adequately powered dedicated trials. Strain-targeted RCTs with standardized outcomes, formal GRADE appraisal, and adequate power are needed before clinical recommendations can be made. Full article

Large Language Models (LLMs) used for clinical decision support must not only make accurate predictions but also generate rationales that are consistent with, and sufficient for, those predictions. Building on Reason2Decide, a two-stage rationale-driven multi-task framework, we propose Reason2Decide-C (R2D-C, where C denotes cycle consistency), which augments Reason2Decide’s stage 2 training with confidence-adaptive scheduled sampling and cycle-consistent rationale-to-label training. In stage 1, we pretrain our model on rationale generation. In stage 2, we jointlytrain on label prediction and rationale generation, gradually replacing gold labels with model-predicted labels based on confidence. Simultaneously, we feed the rationale logits back into the model to recover the label, thus enforcing explanation sufficiency. We evaluate R2D-C on one proprietary triage dataset, as well as public biomedical QA and reasoning datasets. Across model sizes, R2D-C substantially improves rationale–prediction consistency (where stage 1 and stage 2 predictions agree) and sufficiency (where the rationale alone recovers the ground-truth label) over other baselines while matching or modestly improving predictive performance (F1); in several settings R2D-C surpasses $40\times$ larger foundation models. Ablations confirm that the full combination is optimal, maximizing alignment and LLM-as-a-Judge rationale quality. These results demonstrate that confidence-adaptive scheduled sampling and cycle-consistent rationale-to-label training substantially enhance explanation alignment without sacrificing accuracy. Full article

As a vital part of urban public transportation system, subway passenger flow prediction plays a crucial role in alleviating traffic congestion, improving transportation infrastructure, and optimizing travel experience. Existing subway passenger flow prediction mainly focuses on short-term predictions of inbound/outbound passenger flow and origin-destination (O-D) demand. Subway section passenger flow prediction can provide a more direct reflection of passenger fluctuations across different line segments, and offer robust support for management and resource allocation. We propose a subway section passenger flow generation model and a prediction method based on LTiT (LSTM-TSSA-iTransformer). This model is based on the overall architecture of the iTransformer encoder, and an LSTM (Long Short-Term Memory) network is employed to capture the temporal characteristics of subway section passenger flow. This is combined with the TSSA (Token Statistics Self-Attention) to adaptively weight the information at key time points. Efficient performance of the model was evaluated by comparing its predictions with other models, including SARIMA (Seasonal Auto-Regressive integrated moving average), BP neural networks, LightGBM (Light Gradient Boosting Machine) and LSTM (Long Short-Term Memory). Experimental results show that the proposed model outperforms traditional baseline models in evaluation metrics such as R², MAE, MSE, and MAPE. Finally, we further investigate the selection of input window length and prediction step size, and perform robustness analysis under different noise conditions. Full article

Chromosome foreground segmentation is a binary semantic segmentation problem that serves as a prerequisite for overlap reasoning, contact-region inspection, and automated karyotyping. Although simpler than full instance separation in formulation, it remains difficult in metaphase imagery because chromosomes are elongated, deformable, weakly bounded, and frequently touching or partially overlapping. To address these chromosome-specific difficulties, we present DCN-KUnet as a task-oriented integration rather than a new generic segmentation family. The encoder–decoder backbone embeds DCNv3 modules to perform geometry-adaptive sampling for bending-aware and boundary-aware representation learning, while a B-spline KAN bottleneck refines the compressed semantic representation through lightweight nonlinear transformation. In addition, a hybrid objective composed of mask supervision, semantic consistency regularization, and internal feature regularization (${\mathcal{L}}_{cd}+{\mathcal{L}}_{SCR}+{\mathcal{L}}_{IFD}$) jointly constrains prediction accuracy, cross-stage semantic agreement, and feature compactness during training. Experiments on the public overlapping-chromosome dataset and on AutoKary2022 converted to binary foreground masks show that DCN-KUnet achieves stronger Dice, IoU, and HD95 with a moderate parameter budget. These results support the proposed framework as a practical and lightweight semantic foreground front-end for chromosome analysis pipelines rather than a full instance-disentanglement solution. Full article

Background/Objectives: Despite extensive research on nurses’ knowledge and attitudes toward pain management globally, limited evidence exists regarding the actual implementation of multimodal pain management practices among Vietnamese nurses. This study aimed to (1) assess nurses’ implementation of pharmacological and non-pharmacological postoperative pain management interventions, (2) examine the relationships among knowledge, attitude, and practice (KAP), and (3) identify predictors of competent practice with attention to the relative contributions of formal training versus clinical experience. Methods: A cross-sectional survey was conducted among 230 nurses working in Urology Departments from two tertiary public hospitals in Ho Chi Minh City, Vietnam, between April and June 2024, focusing on postoperative pain management. Pain management knowledge, attitudes, and practices were assessed using validated instruments. Independent samples t-tests compared trained versus untrained nurses. Multiple linear regression identified predictors of practice competency. Effect sizes (Cohen’s d) quantified the magnitude of training effects. Results: Nurses demonstrated moderate-to-good competency, with pharmacological interventions (M = 3.74) implemented more consistently than non-pharmacological interventions (M = 3.48, p < 0.001). Trained nurses significantly outperformed untrained nurses across all domains with large effect sizes (Cohen’s d = 1.34–1.54). A clear hierarchy emerged in non-pharmacological practice: environmental (M = 4.01) > physical (M = 3.69) > cognitive–behavioral (M = 3.27) > spiritual (M = 2.60). Strong KAP correlations were observed (r = 0.70–0.85, p < 0.001). Prior training was the strongest predictor of both pharmacological (β = 1.31, p < 0.001) and non-pharmacological practice (β = 0.58, p < 0.001), while clinical experience showed no significant effect (p > 0.40). Conclusions: This study provides evidence that formal training—not clinical experience—is strongly associated with competent postoperative pain management practice among Vietnamese nurses, with large effect sizes demonstrating practical significance. The strong KAP relationships support targeted educational interventions addressing knowledge gaps to improve practice. These findings have implications for nursing education research in Vietnam and similar healthcare settings. Full article