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Case-based evaluation of emergency parking bay installation in three-lane loess tunnels in the loess plateau of Shaanxi Province

Association of troponin-defined myocardial injury with adverse long-term survival among patients with chronic kidney disease

Objective Chronic kidney disease (CKD) is associated with increased mortality, however, the impact of troponin-defined myocardial injury within this population remains poorly understood. This study aimed to investigate the associations between troponin-defined myocardial injury and long-term mortality in CKD patients. Methods This observational study analyzed 22,772,953 weighted records of adult CKD patients from National Health and Nutrition Examination Survey (1999–2004) databases. Myocardial injury was defined by at least one elevated high-sensitivity troponin (hs-cTn) assay, present in 26.6% of the cohort. Cox regression models adjusted for baseline characteristics and comorbidities were used to assess the associations between troponin-defined myocardial injury and all-cause and cardiovascular mortality. Sensitivity analyses excluding patients with known cardiovascular disease (CVD) were performed to evaluate the robustness of the findings. Results Patients with troponin-defined myocardial injury were older and had a higher prevalence of CVD, hypertension, diabetes, as well as a lower estimated glomerular filtration rate (eGFR), compared with those without troponin-defined myocardial injury. Over a median follow-up of 11.6 years, survival was significantly worse among patients with troponin-defined myocardial injury at 1, 5, 10, and 15 years. The adjusted hazard ratios (aHR) for all-cause mortality and cardiovascular mortality in patients with troponin-defined myocardial injury were 1.81 (95% CI 1.51–2.17) and 2.03 (95% CI 1.47–2.79), respectively. Sensitivity analysis excluding records with pre-existing CVD showed similar trends, with an aHR of 1.86 (95% CI 1.56–2.21) for all-cause mortality and 2.44 (95% CI 1.83–3.24) for cardiovascular mortality. Conclusion As a marker for troponin-defined myocardial injury, hs-cTns were independently associated with worse long-term survival among CKD patients. However, the observational design precludes causal inference, and single time-point troponin measurements limit the assessment of dynamic changes in myocardial injury.

A machine learning and agent-based modeling framework for tourism crisis management

Abstract This study proposes and tests a new Destination Resilience Assessment Framework (DRAF) to overcome the weaknesses of existing models which have a limited focus on multi-hazard prediction and on multi-layered interactions of stakeholders/infrastructure/markets during recovery in the context of cascading crises (pandemics, natural disasters and geopolitical shocks). The research employs a mixed-methods design that combines decision-tree based machine learning (random forests), an LSTM-based neural network, and ensemble approaches with agent-based modeling in NetLogo. Simulations are informed and calibrated using real-time streams from 847 sources globally (2019–2024), such as economic indicators, tourist flows, infrastructure capacity and stakeholder sentiment, combined with case data for 12 crises. DRAF achieves 94.7% accuracy in predicting recovery trajectories, identifies key resilience variables with 89.3% accuracy, and forecasts recovery time within ± 2.3 months. Simulation-based counterfactual analyses indicate that destinations implementing DRAF would recover an estimated 67% faster and regain visitor confidence an estimated 43% more quickly than the simulated baseline scenario.The five principal resilience factors are adaptive governance (β = 0.847), stakeholder collaboration networks (β = 0.723), infrastructure flexibility (β = 0.692), market diversification (β = 0.634), and community engagement (β = 0.587). Conceptually, the framework advances tourism crisis management models and, practically, provides transparent decision support that empowers stakeholders to assess impacts and coordinate evidence-based recovery actions.

An innovative quantum–fuzzy paradigm for time- and context-sensitive membership: Quantive logic

Uncertainty in real-world decision problems is rarely static or one-dimensional: the relevance of evidence changes over time, depends on context, and is shaped by interactions between multiple criteria. Classical fuzzy logic provides a flexible scalar notion of membership, but it typically treats each criterion in isolation and does not natively encode temporal or contextual dynamics. Quantum-inspired models, on the other hand, offer rich Hilbert-space representations but are often difficult to integrate with everyday decision-making tasks. This paper proposes Quantive Logic (QL), a quantum-inspired fuzzy framework for time- and context-sensitive membership. In QL, each element is assigned a quantive membership state, represented as a vector in a complex Hilbert space. Conventional fuzzy degrees are recovered as suitable projections of this state, while phase and superposition capture interactions and context effects between criteria. We formalize how quantive membership states are initialized from classical information and updated through linear operators that model temporal evolution and contextual shifts. To illustrate the framework, we outline a multi-criteria credit-risk assessment scenario in which applicant profiles are encoded as quantive membership states and updated under changing economic conditions. This example shows how QL can refine risk judgments when interactions between criteria and scenario-dependent effects are important. Rather than competing with existing fuzzy or probabilistic models, QL is intended as a complementary layer that enriches membership representation wherever time, context, and interaction effects cannot be ignored.

Hibiscus derived nitrogen doped carbon nanochains for visual pH sensing and catalytic methyl orange degradation

Abstract The rapid expansion of synthetic azo dye pollution requires the development of multifunctional nanomaterials capable of simultaneous real-time monitoring and active chemical remediation. Herein, we report the one-pot, microwave-assisted hydrothermal synthesis of nitrogen-doped carbon nano-chains (N-CNCs) using Hibiscus sabdariffa botanical waste as a sustainable precursor. Transmission electron microscopy (TEM) confirmed the fabrication of an interconnected, one-dimensional (1D) beads-on-a-string morphology composed of monodisperse beads (2.08–2.96 nm). Selected area electron diffraction (SAED) verified a short-range turbostratic amorphous carbon framework. The synthesized N-CNCs function as a high-performance dual-mode environmental platform, serving as a high-contrast naked-eye pH sensor and an ultra-rapid catalyst for methyl orange (MO) degradation. Catalytic trials demonstrated a clean, systematic elimination of the chromophore within seconds (< 1 s) at environmental extremes, reaching degradation efficiencies of 79.30% (pH 3) and 74.67% (pH 12). Computational insights from Density Functional Theory (DFT) and Density of States (DOS) analysis decoded the underlying quantum logic, revealing a dramatic collapse of the frontier molecular orbital energy gap (E g ) from 8.5416 eV in native N-CNCs to an ultra-reactive 0.8816 eV within the hybrid matrix. DOS spectra confirmed intense orbital crowding near the Fermi level, which drives instantaneous, non-radiative intramolecular electron transfer for irreversible azo-bond cleavage. This metal-free, circular-economy platform successfully bridges real-time optical tracking with high-capacity chemical remediation, offering a highly competitive blueprint for advanced wastewater treatment.

Assessing the effects of virtual reality-based positive psychotherapy on emotion, life satisfaction, and suicidal ideation in major depression: A mixed-methods randomized controlled trial

Background Positive psychotherapy (PPT) targets the enhancement of well‑being in individuals with major depressive disorder (MDD), and virtual reality (VR) offers an immersive medium for delivering such experiential interventions. However, most VR-based approaches for depression are grounded in cognitive‑behavioral frameworks, leaving the application of PPT within VR environments largely unexplored. This study examined the effects of VR-based PPT on emotional experiences, life satisfaction, and suicidal ideation in individuals with MDD. Methods In this sequential explanatory mixed‑methods study, 78 patients were randomly assigned to VR-based positive psychotherapy (intervention; n = 39) or an equivalent face-to-face format (control; n = 39). Both groups received three sessions of positive psychotherapy, differing only in delivery mode. The quantitative phase assessed changes in affect, life satisfaction, and suicidal cognitions before and after each session using the PANAS, SWLS, and B-SCS. A subsequent qualitative analysis of the intervention group was conducted to contextualize and further interpret the quantitative findings. Results The results demonstrated a significant multivariate effect over time (Wilks’ Lambda = 0.316, F(20,57) = 6.176, ɳ² = 0.684, P < 0.001), indicating significant improvements in the outcome measures across sessions. Within‑group effect sizes ranged from small to moderate (d = 0.02–0.53), with comparable improvements observed in both the VR-supported and face-to-face formats. Qualitative findings further enriched the interpretation of these patterns, yielding two overarching themes: “user-friendly program” and “efficient psychotherapy program.” Conclusion Both VR-based and face-to-face positive psychotherapy were associated with improvements in emotional experiences, life satisfaction, and suicidal ideation in individuals with major depressive disorder. VR-based delivery appeared feasible and acceptable, with outcomes comparable to the traditional face-to-face format in this preliminary trial. Further studies with larger samples and longer follow-up are needed to confirm these findings and clarify their clinical implications. Trial registration: This trial was prospectively registered in the Iranian Registry of Clinical Trials (IRCT20201001048893N7, registration date: 16 November 2022, URL: https://irct.behdasht.gov.ir/trial/66424 ) prior to the enrollment of the first participant.

Microbacterium pollutisoli sp. nov., isolated from HCH-contaminated soil, exhibits genomic potential for bioactive compounds

Overexpression of CD97 in intestinal epithelial cells attenuates LPS-induced pro-inflammatory cytokine induction via stabilization of β-catenin early in life

Acute inflammatory conditions in the intestine of preterm infants are linked to increased susceptibility due to the immature degree of the gut’s epithelial barrier, microbiota, and pattern recognition receptors. Toll-like receptor 4 (TLR4) plays a pivotal role in recognizing lipopolysaccharides (LPS) from gram-negative bacteria, triggering pro-inflammatory cytokine responses (TNF-α, IL-1β, CXCL1) via nuclear factor kappa B (NF-κB) signaling. These processes are implicated in necrotizing enterocolitis (NEC), a severe gastrointestinal condition in premature infants. CD97, an adhesion G-protein coupled receptor (aGPCR), has emerged as a modulator of immune responses influencing inflammatory signaling pathways. CD97 expression is typically low in intestinal epithelial cells (IECs); however, protective effects of increased CD97 levels have been described in experimentally induced colitis. In this study, we examined the role of CD97 in modulating the inflammatory response in the immature gut, utilizing wild-type (WT) and transgenic CD97-overexpressing mice (TgCD97) with epithelial-specific expression in the intestinal epithelium. LPS was administered to IECs and organ segments isolated from the small intestine of mice of specific ages, modeling certain stages of human perinatal/postnatal gut development. CD97 overexpression attenuated LPS-induced TNF-α expression in IECs during early intestinal development while IECs from adult mice remained unaffected. This effect was attributed specifically to ileal tissue. Attenuation was mediated by β-catenin stabilization, leading to suppressed LPS/NF-κB signaling. Inhibition of β-catenin in TgCD97 IECs reversed the anti-inflammatory phenotype restoring pro-inflammatory gene expression. These findings suggest that CD97 overexpression modulates the inflammatory response in the developing gut by stabilizing β-catenin and interacting with the LPS/NF-κB axis. This effect was restricted to early developmental stages and ileal tissue, highlighting a previously unrecognized role of CD97 in age-dependent endotoxin tolerance. These findings identify a novel CD97-β-catenin-NF-κB regulatory axis in the immature small intestine and highlight its potential as a therapeutic target to protect the immature neonatal gut from inflammatory damage.

Efficacy and safety of trastuzumab emtansine versus trastuzumab deruxtecan in HER2-positive breast cancer with anti-HER2 therapy resistance: a multicenter retrospective study

The promise of deep urine proteomics for diagnosis of cancer, neurologic, and metabolic diseases

Introduction Urine offers a noninvasive and low-cost source of disease biomarkers, yet most proteomic studies have targeted single conditions. Using deep proteomic profiling and machine learning, we evaluated whether urinary protein signatures distinguish early-stage cancer, neurologic, and metabolic diseases from healthy controls. Methods This case–control diagnostic accuracy study analyzed urine samples from the Ukraine Association of Biobank (UAB), collected during routine medical check-ups. The study included 22 patients each with kidney, bladder, melanoma, prostate, ovarian, endometrial, and cervical cancers; 22 with multiple sclerosis (MS); 22 with metabolic dysfunction–associated steatohepatitis (MASH); and 66 healthy controls, yielding 264 samples analyzed in September 2023. Proteomic assays were performed using the Olink Explore 3072 platform, with laboratory personnel blinded to disease status. Urine proteomes were profiled to identify disease-specific protein signatures. The primary outcome was diagnostic accuracy of multiprotein urine panels for each disease compared with healthy controls, expressed as the area under the receiver operating characteristic curve (AUC), along with sensitivity and specificity calculated at a prespecified threshold. Results Expected sex‑specific differences (KLK3, MSMB higher in males; KLK8, KLK13 higher in females) supported assay validity. Three expression patterns were observed: (1) few strong, symmetric signals in melanoma and endometrial cancer; (2) asymmetry with many up‑regulated proteins in cervical, ovarian, and prostate cancers and in MS; and (3) broad up‑regulation in kidney and bladder cancers and in MASH. Multiprotein models outperformed single proteins, plateauing at five to seven. Maximum AUCs ranged from 0.88 (MS) to 0.98 0.97 (ovarian cancer), with AUCs ≥ 0.95 for seven of nine diseases. Some proteins (e.g., C9orf40, PPY) showed cross‑disease importance. Conclusions Urine proteomics identified disease‑related signals across cancer and metabolic conditions and may enable accurate, noninvasive classification using multiprotein panels, but given the exploratory design and its limitations, the reported accuracies should be regarded as upper-bound estimates requiring prospective validation.

Expression characteristics and clinical significance of PSCA in pancreatic cancer

Explaining the reasons for the divorce of young couples in Western Iran: A qualitative Study from the Beneficiary's point of view

Divorce is an important social issue that has destructive consequences for both the individual and the family. It is a multi-dimensional and complex phenomenon that occurs under the influence of various factors, so this research was conducted to explain the reasons for divorce among young couples in the West of Iran with a qualitative approach. This research was carried out with a qualitative approach and conventional content analysis. Purposeful and snowball sampling were used to identify the participants. Semi-structured interviews were used to collect data and continued until theoretical saturation was reached. In this research, 16 key informants and 32 main participants were present. For data analysis, Graneheim and Lundman method was used. Also, to increase the quality of the research, Guba and Lincoln's four criteria were observed. 212 primary codes, 24 subcategories, and 3 categories came from the analysis of the interviews. Categories and sub-categories include 1- individual factors (sexual problems, mental-psychological disorders, spouse's behavioral characteristics, individualism, physical appearance problems, Formation of a positive understanding of divorce consequences, having defective relationships before marriage, drug use), 2- socio-cultural factors (de-tabooing of divorce, women-oriented social changes, media and social networks, lack of a suitable support system, inappropriate spouse choosing, economic challenges, modeling of divorce), 3- family factors (job's impact on family life, improper communication with spouse's family, accumulation of negative feelings in marital life, issues related to children, cheating on spouse, inefficient relationships, heterogeneity of lifestyle, violence, inability to manage challenges). The results indicate that divorce is a complex, multi-dimensional phenomenon influenced by individual, social, cultural, and family factors. Prevention requires multi-level interventions, including educating couples on sexual issues and communication skills, promoting responsible marital roles, preventing superficial marriages, reducing stigma around seeking psychological help, managing personality and lifestyle differences, guiding families on conflict resolution, and providing access to counseling and mental health services before and after marriage.

Nanomagnetic cyanuric chloride supported Cu as an efficient and reusable nanocatalyst for the synthesis of ortho aminocarbonitriles

Depressive symptoms among older adults in Turkey: Evidence from a nationally representative ageing survey

Background Late-life depression is a growing public health issue in ageing societies, influenced by health, functional, socioeconomic, and psychosocial factors. Evidence integrating health, functional, psychosocial, and healthcare-access determinants using nationally representative data in Turkey remains limited. This study examined determinants of depressive symptoms among adults aged ≥65 years. Methods This study analyzed data from 10,348 participants aged ≥65 years from the nationally representative Turkey Older Persons Profile Survey (TYPA 2023). All analyses incorporated survey sampling weights. Depressive symptoms were defined using the Geriatric Depression Scale-30 (GDS-30) (cut-off ≥10). Hierarchical logistic regression models were constructed in sequential blocks (sociodemographic; health and functional status; psychosocial and healthcare-related factors) to identify independent correlates. Results The weighted mean age was 72.8 years; 55.1% were women, and 46.2% screened positive for depressive symptoms according to the applied screening threshold. In the fully adjusted model, depressive symptoms were associated with female sex, lower education, unmarried status, poorer self-rated health, sensory and functional limitations, perceived age-related restriction, perceived exclusion, and difficulties accessing or communicating with healthcare services. Conclusions Depressive symptoms among older adults in Turkey reflect the combined influence of health decline, functional limitations, negative psychosocial perceptions of ageing, and barriers to healthcare access. Interventions addressing functional decline, financial strain, ageism, and access barriers may support better mental health in later life and inform public health and ageing-related policies.

MRI-derived 3D lower limb muscle shape as a biomarker for disease severity in Duchenne muscular dystrophy

Abstract Duchenne muscular dystrophy (DMD) is characterized by progressive muscle degeneration leading to loss of ambulation. Identification of predictive biomarkers of loss of ambulation is crucial, yet analysis of muscle shape remains underexplored. Using MRI data from 17 children with DMD (10 followed by loss of ambulation) and 10 healthy controls, we analyzed the muscle shapes and volumes of the lower limbs. Correlations with functional metrics (gait tests, dynamometric forces) were evaluated, and exploratory predictive modeling tasks (classification and regression) were performed via random forests with cross-validation. Compared with controls, key muscle groups, particularly the triceps surae, in individuals with DMD presented distinct shape patterns, including increased thickness and reduced extensibility, without consistent differences in absolute volume. In cross-validated analyses and within the limits of this small cohort, models based on shape descriptors showed high accuracy for distinguishing controls from DMD and yielded a mean absolute error of approximately 110 days for time-to-ambulation-loss prediction. These findings suggest that geometric descriptors may provide complementary, acquisition-agnostic information to MRI fat-related measures, but require validation in larger, independent cohorts before any clinical use is considered.

Walking the tightrope of justifiable decision‑making: An exploratory qualitative study identifying barriers and solutions to efficient safety reporting

Background Safety reporting is integral to clinical trial conduct, aiming to protect the rights and safety of trial participants and future patients. Over time, reporting safety events has become increasingly complex, leading to inefficiencies which place extra burden on trial staff and potentially impact patient safety. Attempts have been made to streamline these processes. However, effecting change in practice is challenging. Changes to UK trial regulations introduced in 2025 may reduce inefficiencies. To incorporate these changes effectively, we need to understand what barriers exist for their implementation. This study aimed to identify barriers and solutions to efficient safety reporting processes from the perspective of staff working at registered academic trials units in the UK, with a view to developing recommendations to enable the delivery of more efficient safety reporting practices. Method This was an exploratory, observational, qualitative focus group study of trials unit staff across the UK. Verbatim transcripts of the focus group discussions were analysed using Reflexive Thematic Analysis. Results Twenty-three CTU staff participated in four focus groups. One over-arching theme was generated from the analysis, “ Walking on a tightrope: Making justifiable decisions”. Participants felt that they were performing a balancing act between efficient, risk-proportionate safety reporting and risk-aversion. Possible solutions included clarification of expectations and improved transparency, and improved training and knowledge building. Discussion Despite efforts to streamline safety reporting processes, problems persist with the implementation of risk-proportionate approaches. Concerns that decisions will be deemed inadequate and impact patient safety and well-being make decision-making a difficult balancing-act. Confident decision-making by trial practitioners can be facilitated through access to resources and training provided by regulators, supported by tools and case studies. Trials unit level actions, such as mentoring schemes or platforms to share knowledge and learning, can support knowledge building and confidence development.

Correction: On the Hydrogen Bond Strength and Vibrational Spectroscopy of Liquid Water

Educational strategies to improve health literacy for people with type 2 diabetes in low socio-economic communities: A realist review protocol

Introduction Type 2 diabetes (T2D) has emerged among the top ten causes of disability and mortality worldwide. Health literacy is crucial for effective self-management to reduce the burden associated with T2D. Studies have reported the effectiveness of educational strategies for improving health literacy to promote good health and well-being. However, contextual factors influence the effectiveness of these strategies. Therefore, this paper intends to explain how and why context shapes the mechanisms through which educational strategies work to improve health literacy for adults with type 2 diabetes in low socioeconomic communities. Methods and analysis Theory-driven realist review methods will explain how and why contexts activate different mechanisms through which educational strategies work to produce intended or unintended outcomes in low socioeconomic communities. The following five steps of realist review, which are non-linear and iterative, will be undertaken: (i) Define the scope of the review and locate existing theories on educational strategies to improve health literacy, (ii) Develop the initial programme theories, (iii) Search for evidence, (iv) Select papers and appraise, and (v) Extract data and synthesis. The following databases will be searched, but not limited to, PubMed, Education Resource Information Centre (ERIC), and PsycINFO. Papers will be selected based on relevance, richness, and rigour. Data extraction will follow both inductive and deductive approaches. The Intervention-Context-Actor-Mechanism Outcome (ICAMO) configurations will be utilised to analyse and synthesise data using retroductive reasoning. Finally, the revised programme theories, which explain how the educational strategies are expected to work across different contexts in low socioeconomic communities, will be prepared and disseminated. Conclusion The findings may inform practice, influence policy, and contribute to the design of health literacy programmes in similar settings. The realist review is registered with the Open Science Framework: ( https://osf.io/9w867 )

Nonlinear speaker–listener age interactions shape how listeners’ perceived emotions and voice impressions mediate purchase intention in advertising speech

Development of a scale for measuring the perception of artificial intelligence among mental health consumers

Background Artificial Intelligence (AI) has emerged as a transformative force revolutionizing various sectors, including healthcare, particularly the mental health field. However, the acceptance and integration of AI technologies in different healthcare systems can be influenced by various factors, including cultural, social, and individual aspects. Nevertheless, there is a need for a valid and reliable tool for assessing AI’s perception among healthcare consumers. Aim To develop and validate a tool for the perception of AI among healthcare consumers and apply the tool to assess AI’s perception among mental health consumers in the Jordanian healthcare system. Method A cross‐sectional descriptive correlational design was utilized in the study. Data was collected from a convenience sample of 431 mental health consumers visiting mental health clinics of the International Medical Corps and university hospitals in Jordan. Structured interviews were conducted using an AI Perception (AIP) questionnaire developed by the authors. The questionnaire’s content validity was assessed by an expert panel. Using Principal Component Analysis (PCA), the construct validity of the tool was evaluated, and its internal consistency was examined using Cronbach’s alpha. Descriptive statistics were used to assess the levels of AI perception among participants. Results The final AIP tool consisted of 20 items across 4 domains and has demonstrated strong internal consistency across its four domains: AI acceptance and readiness (α = 0.92), AI perceived importance (α = 0.92), AI perceived risk (α = 0.9), and AI perceived challenges (α = 0.85). The construct validity of the four-domain structure of the tool was supported by PCA. Additionally, the mean scores for each domain indicated the average level of agreement with AI perception items among participants. Specifically, the mean score for AI acceptance and readiness was ( 2 . 7   ±   0 . 96 ). AI perceived importance was (2.18 ±   0.83), AI perceived risk was (2.58 ±   0.92), and AI perceived challenge was (2.78 ±   0.87). Conclusion The findings of this study resulted in developing a valid and reliable 20-item tool to assess AI’s perception among mental health consumers. The tool can be used to assess the predictors of AI’s readiness among mental health consumers. Therefore, aiding policymakers and other stakeholders in understanding the AI adoption barriers from the perspective of end-users. In addition, this study developed the AIP tool that can be validated and used among other populations in future research.