Browse Articles

Alterations in sensory and motor nerve conduction associated with age and osteoarthritis in cats are correlated with functional impairment and somatosensory sensitization

Abstract Feline somatosensory impairments associated with aging and osteoarthritis (OA) can be quantified through quantitative sensory testing and nerve conduction (NC). This prospective, randomized and blinded study aimed to compare NC in young healthy (CTRL, n  = 6) and older OA (OLD, n  = 12) cats. Under temperature-controlled general anesthesia, amplitude and conduction velocity (CV) of mixed tibial/sciatic and ulnar nerves were measured. Structural changes (radiographic score), functional alterations [MI-CAT(V)], neuro-sensitization [paw withdrawal threshold (PWT); and response to mechanical temporal summation (RMTS)] were assessed. Data distribution determined tests for inter-group comparison, and outcome correlations (α = 5%). OLD cats presented higher radiographic and MI-CAT(V) scores, but lower PWT, RMTS, motor CV (–23%), motor (–35%) and sensory (–45%) amplitudes compared to CTRL. Functional alterations correlated strongly with structural changes ( r  = 0.74, p  < 0.001) and age ( r  = 0.90, p  < 0.001), and moderately with neuro-sensitization ( p  < 0.016). Radiographic and MI-CAT(V) scores correlated negatively with (distal) motor CVs ( p  < 0.044), and with some motor and sensory amplitudes ( p  < 0.028). The PWTs correlated positively to motor CVs ( p  < 0.045). Older OA cats exhibited NC alterations correlated with neuro-sensitization and higher than expected with age alone.

Caputo fractional-order SVIR model for rotavirus: Numerical solutions using Laplace-Adomian decomposition method

Rotavirus is a leading cause of severe gastroenteritis and diarrheal mortality in children under five years of age, especially in developing countries. Mathematical modeling plays a crucial role in understanding the transmission dynamics of rotavirus and in evaluating the impact of vaccination strategies. In this study, a Caputo fractional-order susceptible–vaccinated–infected–recovered (SVIR) epidemic model is proposed to explore the transmission dynamics of rotavirus while capturing memory and hereditary effects associated with disease progression and immune response. The disease-free and endemic equilibrium points of the model are derived, and the vaccination reproduction number R v is obtained using the next-generation matrix technique. Stability analysis is performed for the disease-free and endemic equilibrium points. In addition, a comparative study for R v < 1 and R v > 1 is presented. The sensitivity of the model parameters is computed, and the results are presented graphically. Also, the positivity and boundedness of the solutions are verified to ensure biological feasibility. The approximate solutions of the Caputo fractional-order SVIR model are obtained using the Laplace Adomian Decomposition Method (LADM). The stability, convergence, and error analysis of this well-established method are also studied. To validate the obtained solutions, the method is compared with other methods in the classical-order case. Additionally, the LADM solutions are presented numerically and graphically for different fractional orders, showing that reducing the fractional-order parameters increases memory effects and significantly changes the epidemic dynamics. The numerical and graphical results confirm that the fractional-order framework captures the dynamics of the proposed model more effectively than the corresponding classical integer-order epidemic model.

Associations of high-carbohydrate and high-fat dietary patterns with obesity in Thai adults: A cross-sectional analysis with gender comparisons

Accuracy of the molecular diagnosis of duchenne and becker muscular dystrophy: A systematic review with meta-analysis

Introduction Recently, Molecular diagnosis of Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) has become increasingly important in the management of these patients, with techniques such as multiplex ligation-dependent amplification (MLPA) and next-generation sequencing (NGS) coming to the fore. Therefore, this study aims to evaluate the diagnostic accuracy of MLPA, NGS, and the algorithm MLPA-NGS for confirmatory diagnosis of DMD/BMD. Methods We systematically searched databases (PubMed, Embase, Scopus, Cochrane and Web of Science) until July 2025 for studies evaluating the diagnostic accuracy of MLPA and/or NGS testing in patients with clinical suspicion of DMD, considering multiplex PCR or biopsy as the reference test. A meta-analysis was performed using a random-effects model to estimate the sensitivity, specificity, and detection rate of each test. The QUADAS-2 tool was used to assess the risk of bias and the GRADE criteria were used to identify the certainty of evidence. Results We included 10 studies (3786 patients) evaluating the use of MLPA and 14 studies (4333 patients) evaluating the use of NGS. For MLPA, the sensitivity was 0.80 (95%CI: 0.76–0.84; I 2 : 86%), the specificity was 0.93 (95%CI: 0.87–0.96; I 2 : 16%), and AUC 0.90 (CI-95%: 0.89–0.92). For NGS, the detection rate was 0.77 (95% CI: 0.61–0.87; I 2 : 94%). Furthermore, the detection rate increased to 0.97 (95% CI: 0.94–0.99; I 2 : 95%) when NGS was performed after MLPA. We observe a low risk of bias but with very low certainty in the estimations. Conclusions In patients with clinical suspicion of DMD, the MLPA test is very good but with very low certainty. However, in these patients with a negative MLPA, adding the NGS test would allow improve the detection rate. Therefore, the sequential use of these tests could be considered in patients who persist in the clinical suspicion.

Safety and efficacy of the combination of hyperbaric oxygen and 177Lu-DOTA-IBA in treating metastatic bone pain due to tumors

Emerging role of human endogenous retroviruses in mothers with history of perinatal depression and risk of neurodevelopmental disorders in offspring: Results from a pilot study

Perinatal depression (PD) actually affects 10−15% of pregnant women and represents one of the most debated topics as potentially implicated in offspring neurodevelopmental disorders etiology, particularly Autism Spectrum Disorder (ASD). Scientific evidence supports the role of Human Endogenous Retroviruses (HERVs) in ASD, as a link among environmental stimuli, epigenetic remodeling and biological processes. The aim of the present study was to characterize the expression profile of different HERVs and selected cytokines in peripheral blood mononuclear cells from women who have experienced PD in comparison to women without history of PD and their respective children stratified according to ASD diagnosis, by RT Real-Time PCR. We showed that ASD children and their PD mothers share abnormal expression of pHERV-W, syncytin-2 and IL-6 likely influenced by the common environment, maternal status, genetic predisposition, or postnatal factors. Of note, mothers with a history of PD were also evaluated at the time of blood sampling using the Hamilton Depression Rating Scale, and a positive correlation with pHERV-W levels was observed. Together with previous results in preclinical models and human studies, our results support the role of HERVs in autism as a contributing factor in creating an adverse environment for normal neurodevelopment, strengthening the view of a mother-child association in the context of autism. PD being associated with the altered activity of HERVs could be considered to be an additional risk factor in the pathogenesis of autism.

A unified framework for net effects in signed social and ecological networks

Abstract With improvements in data resolution and quality, researchers can now represent complex systems as signed, weighted, and directed networks. In this article, we introduce a framework for measuring net and indirect effects without simplifying these information-rich networks. It captures both direct and indirect interactions, the effect of the whole network on a node, and conversely, the effect of a node on the entire network, while accommodating the complexity of signed, weighted, and directed links. Our taxonomy unifies and extends existing approaches and measures from network science, computational social science, and ecological networks. We demonstrate its value in ecological systems, where net and indirect effects are critical yet difficult to quantify. Using generalized Lotka–Volterra dynamics, we find a strong correlation between negative net effects and species extinction. We further apply the framework to a real-world social network, where it identifies informative rankings that illuminate influence propagation and power dynamics.

Associations between self-reported symptoms and circulating protein biomarkers: A scoping review protocol

Background Studies linking self-reported symptoms to circulating protein biomarkers are increasing, partly driven by the rising number of protein biomarkers being identified. Despite research advances, current reviews that synthesise these studies are typically disease-specific. A broader, diagnosis-agnostic and multidisciplinary scoping review can uncover shared biological patterns associated with symptoms across different conditions and can generate insights that advance precision health and symptom science. This scoping review protocol builds on this need for a broad approach as it aims to consolidate current knowledge on self-reported symptom and circulating protein biomarker associations, identify potential patterns, and provide relevant insights for clinical practice and future research. Methods The protocol is aligned with the PRISMA and JBI guidelines for scoping reviews. The search strategy includes original peer-reviewed journal research articles examining associations between self-reported symptoms and protein biomarkers in blood plasma or serum. Searches will be conducted from PubMed, CINAHL, Embase, and Web of Science databases. A preliminary search retrieved more than 30,000 articles, prompting a pilot test of the Artificial Intelligence (AI)-assisted review screening tool ASReview. The pilot demonstrated time savings, refined methodological decisions, and confirmed the feasibility of proceeding with the review. Therefore, screening will be conducted using ASReview, with a total of five reviewers involved in the process. Data extraction will focus on single self-reported symptoms and circulating protein biomarker pairs that have a reported association. Analysis will involve counting and mapping these associations to identify potential patterns. The protocol was registered with the Open Science Framework (OSF) https://osf.io/bku3f . Discussion This protocol provides a structured and transparent approach for conducting a large-scale scoping review. By adhering to established guidelines, the protocol provides a more comprehensive, standardised, exact, and reproducible accumulation of knowledge, while acknowledging potential limitations. The expected results can contribute to summarise the current understanding of associations between self-reported symptoms and circulating protein biomarkers. This fosters the integration of symptom science and the omics field, specifically proteomics, to advance precision health.

Effects of dietary pretreated anchovy waste on growth performance, meat composition, haematology and gut histology in quail

Spatial distribution and associated factors influencing 2024 Measles-Rubella vaccination campaign coverage among children aged 9–59 months in Mainland Tanzania: A multi-level mixed-effect analysis

Introduction Globally, measles remains a major cause of child mortality, and rubella is the leading cause of birth defects among all infectious diseases. In Mainland Tanzania, eliminating measles and rubella remains challenging due to geographical diversity, uneven healthcare facilities distribution, and socio-economic disparities across regions. Understanding spatial patterns and associated determinants of vaccination coverage is essential for improving campaign effectiveness. This study aimed to explore the spatial distribution and associated factors of measles-rubella campaign coverage among children aged 9–59 months in Mainland Tanzania. Methods A cross-sectional survey was conducted following the implementation of the February 2024 measles-rubella (MR) vaccination campaign to assess the spatial distribution and factors influencing MR vaccination coverage among children aged 9–59 months in Mainland Tanzania. Spatial autocorrelation was evaluated using Moran’s I to detect clustering, while Local Indicators of Spatial Association (LISA) was used to identify High-High and Low-Low clusters. Hotspot and cold-spot analyses were performed using Getis–Ord Gi* statistics at the 95% confidence level , consistent with standard epidemiological reporting practices, to identify statistically significant spatial clusters. To identify factors associated with the campaign coverage, we used a multivariable logistic regression model. Results The study included 16,703 children, of whom 81.5% received the MR vaccine during the campaign. Vaccination coverage varied notably between regions, with Tabora and Pwani having a low coverage rate of 58.8% (95% CI: 51.3%−65.9%) and 61.0% (95% CI: 45.0%−75.0%) respectively. Njombe and Mbeya demonstrated high MR vaccination coverage of 97.4% (95% CI: 90.5–99.3%) and 95.6% (95% CI: 90.9–97.9%), respectively. The household wealth quintile and place of residence, caregiver’s education, caregiver’s age, and their marital status were associated with receiving MR vaccination during the campaign among children aged 9–59 months in Mainland Tanzania. Spatial distribution revealed significant clustering of vaccination coverage (Moran’s I = 0.34, p < 0.01). The LISA identified two distinct categories of clusters: High-High Clusters (high-coverage) and Low-Low Clusters (low-coverage). High-High Clusters, which indicate regions with high MR vaccine rates surrounded by similar neighbors, are concentrated in regions such as Njombe and parts of Mbeya, whereas Low-Low Clusters, representing regions with low MR coverage, are found in areas like Tabora, Katavi, Dar es Salaam and Pwani. The Getis-Ord Gi* hotspot analysis shows significant clustering, with high-confidence hotspots in the southern highlands (Njombe and Mbeya) and notable cold spots in western Tanzania (Tabora and Katavi) and parts of eastern Tanzania. Conclusion This study demonstrates substantial spatial heterogeneity in measles–rubella vaccination coverage across Mainland Tanzania, with persistent geographic inequities driven by socio-economic and demographic factors. These findings demonstrate how integrating geospatial insights with equity-focused planning can support precision public health planning, enabling targeted interventions to close coverage gaps and accelerate progress toward measles and rubella elimination.

Adaptive self-supervised learning for real-time problem solving in autonomous systems

Prediction of factors contributing to Pain Intensity among low back pain patients: A comparative machine learning frameworks (Random Forest versus XGBoost)

Background Predicting pain intensity in patients with low back pain (LBP) remains a complex task due to the biopsychosocial nature of pain. Pain intensity is shaped by multifaceted interactions among demographic, lifestyle, and clinical factors. Aim This study aimed to predict factors contributing to pain intensity in adults with lower back pain (LBP) using Random Forest (RF) and XGBoost models. It evaluated the association between lifestyle factors and lumbar spine MRI abnormalities, classifying pain intensity into strong (NRS 7–8) and very strong (NRS 9–10) categories among patients with lumbar disc disorders. Methods Cross-sectional study of 61 LBP patients (Numerical Rating Scale ≥ 7) at King Fahad Specialist Hospital, Saudi Arabia. Predictors included demographics (age, sex, body mass index), MRI findings (disc location, number of affected levels, pathology type), and lifestyle factors (exercise, sitting time). Random Forest (500 trees, 70/30 train-test split, 5-fold cross-validation) and XGBoost were compared. Results RF achieved accuracy = 0.579 (95% CI: 0.334–0.800), AUC = 0.607 (0.340–0.875), specificity = 0.917, and sensitivity = 0.000. The strongest predictors were number of affected disc levels (MDG = 0.62), L4–L5 disc location (MDA = 0.48), age (0.31), and exercise time (0.28). XGBoost achieved 66.67% accuracy but sensitivity of only 0.33, likely due to class imbalance (72.1% very strong pain). RF outperformed XGBoost in overall stability; XGBoost provided complementary feature-level insights via SHAP. These findings highlight the potential of machine learning as a decision-support tool for identifying pain-related risk factors in LBP. Implications RF demonstrated limited predictive utility in its current form, insufficient for clinical application. Future research should involve multi-center designs with larger sample sizes (n ≥ 200) and address class imbalance prior to considering clinical translation. Perspective This study demonstrates how integrating lumbar MRI findings with machine learning improves pain intensity prediction in low back pain, supporting more objective risk stratification and informed clinical decision-making.

Hybrid quantum-classical neural networks for low earth orbit satellite communications in 6G

CDCA8 regulates ATP5F1A protein stability and malignant phenotypes in wilms tumor cells: Prognostic implications and mechanistic insights

Background Wilms tumor (WT) is a prevalent pediatric renal malignancy, yet its molecular mechanisms remain poorly defined. Identifying key prognostic genes and understanding their functional roles is critical for improving clinical management. This study aimed to identify prognostic genes in WT and to investigate whether ATP5F1A functions as a candidate downstream effector in a CDCA8-associated regulatory axis. Methods Differentially expressed genes (DEGs) were identified by analyzing RNA-seq datasets (GSE11151, GSE73209), comparing WT tissues with normal kidney samples. Functional enrichment was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A protein-protein interaction (PPI) network was constructed, and hub genes were identified using Cytoscape. Prognostic significance was assessed through univariate and multivariate Cox regression analyses, and a risk prediction model was developed. External expression validation of candidate hub genes was performed using GSE11024 and GSE110696. CDCA8’s functional role in WT was validated by gene knockdown and overexpression experiments. Co-immunoprecipitation (Co-IP) and ubiquitination assays were conducted to explore the interaction between CDCA8 and ATP5F1A. Results A total of 987 DEGs were identified, with six hub genes—BUB1B, CDC45, CDCA8, KIF15, NDC80, and TOP2A—were identified. The exploratory risk score model stratified overall survival in the TARGET-WT cohort (HR = 2.973, Log-rank p = 0.000252), although time-dependent AUC values indicated modest discriminatory performance. CDCA8 was upregulated in WT tissues and associated with worse survival outcomes (HR = 2.03, p = 0.011). Functional assays suggested that CDCA8 knockdown suppressed malignant phenotypes in WiT49 cells. Mechanistically, CDCA8 knockdown reduced ATP5F1A protein levels without altering ATP5F1A mRNA expression, and this reduction was partially rescued by MG132. CDCA8 depletion was also associated with increased ATP5F1A ubiquitination, supporting a role for CDCA8 in maintaining ATP5F1A protein stability. Conclusions These findings suggest that CDCA8 may contribute to Wilms tumor cell phenotypes partly by maintaining ATP5F1A protein stability, providing preliminary evidence for a CDCA8-ATP5F1A regulatory axis in WT.

Exploring the role of pain-related fear and lifting biomechanics in predicting low back pain incidence using supervised machine learning

Abstract Greater lifting-specific pain-related fear has been associated with reduced lumbar spine motion during lifting, suggesting fear-driven protective movement strategies with potential negative consequences. However, the role of task-specific pain-related fear and lifting kinematics in the development of low back pain (LBP) remains unclear. This study aimed to develop and evaluate a supervised machine learning model to predict one-year LBP incidence and to identify the most important predictors. Baseline data from 156 healthy participants included pain-related fear, demographic, health, and lifestyle factors as well as lumbar spine range of motion (ROM) and whole-body lifting strategy during 15-kg lifting. LBP incidence was assessed using biweekly follow-up questionnaires. We trained and evaluated an Explainable Boosting Machine to predict one-year LBP incidence from baseline variables. The final model achieved an accuracy of 0.815 and an ROC AUC of 0.839 for out-of-sample predictions. The most important predictors were higher BMI, lower sleep quality, greater lifting-specific pain-related fear, and reduced lumbar spine ROM. This predictive performance suggests that a machine learning approach may help identify individuals at higher risk of developing LBP according to the applied criteria. These findings emphasize the role of lifting-specific pain-related fear and lifting kinematics in the development of LBP and highlight multifactorial contributors to LBP incidence.

Enhancing the sustainability of cultural identity in science curricula through artificial intelligence as an innovative educational approach

This study investigates the role of AI as an innovative educational approach in promoting the sustainability of cultural identity within science curricula. To achieve this aim, a qualitative research design was employed, using semi-structured interviews and classroom observations as primary data collection methods. The study sample consisted of forty-seven science teachers from the Al-Qassim and Al-Ahsa regions in Saudi Arabia, selected purposively based on their experience with technology-enhanced instruction. The collected data were analyzed using systematic qualitative procedures involving open and axial coding, which facilitated the emergence of key categories related to AI integration, cultural relevance, and instructional practices. Accordingly, the analytical process is best understood as an inductive thematic qualitative approach rather than a full grounded theory methodology, as it does not include theoretical sampling, data saturation, or iterative cycles of data collection. This clarification ensures methodological transparency and alignment between design and analysis. Findings derived from both teachers’ perceptions and classroom observations indicate that AI is not perceived merely as a tool for knowledge transmission, but rather as a supportive instructional mechanism that enables the contextualization of scientific concepts within local cultural settings. In addition, AI-supported instructional practices appear to foster more interactive and collaborative classroom environments, while also contributing to more responsive approaches to assessment. However, it is important to emphasize that these findings are based on teachers’ reported perceptions and observed instructional practices rather than direct measurement of student learning outcomes. Despite this limitation, classroom observations suggest that integrating culturally relevant examples, analyzing cultural texts, and designing collaborative, culturally responsive learning activities may enhance student engagement and strengthen their connection to cultural identity within science learning contexts. Overall, the study highlights the potential of AI to bridge scientific knowledge and local cultural heritage in science education. It contributes to the growing body of literature on AI in education by emphasizing its role in fostering culturally responsive pedagogy and enhancing meaningful learning experiences. The study further underscores the importance of providing teachers with targeted professional development in AI integration, developing culturally responsive instructional materials, and designing assessment practices that address both conceptual understanding and cultural dimensions of learning.

Dynamic rough set learning for reliable early warning in industrial time-series systems

Abstract Industrial early-warning systems require models that can detect transitional risk states before failure while also explaining uncertainty in the resulting decisions. Existing machine-learning and deep-learning approaches can achieve strong predictive performance, but they often provide limited information about whether a time-series window is certainly normal, certainly critical or only ambiguously classified. This paper addresses this problem by proposing a dynamic rough set learning framework for uncertainty-aware early warning in industrial time-series systems. The proposed method converts multivariate sensor trajectories into sliding-window decision systems and constructs time-dependent neighborhood rough approximations for evolving decision classes. A rough early-warning index is introduced to quantify boundary-region expansion over time, and a dynamic dependency-based feature-reduction procedure is developed to retain informative window descriptors while preserving rough decision ability. Unlike purely black-box classifiers, the framework provides class predictions together with support gaps, local uncertainty scores, boundary-region information, deferred decisions and warning rates. The framework is evaluated on the NASA C-MAPSS FD001 turbofan degradation benchmark. The full dynamic rough set model achieves a test accuracy of 0.8647, balanced accuracy of 0.7570 and Macro-F1 score of 0.7895. Random Forest and XGBoost obtain stronger pure classification scores, with Macro-F1 values of 0.8715 and 0.8611, respectively. However, the proposed method supplies additional rough uncertainty outputs that are useful for inspecting transitional and ambiguous windows. Dynamic rough feature reduction decreases the feature set from 118 to 40 features while retaining a test accuracy of 0.8459 and Macro-F1 score of 0.7564. Robustness analysis further shows that the model is stable under moderate Gaussian feature noise, whereas high missingness mainly affects the warning class. These findings indicate that the proposed framework is most suitable for early-warning settings where interpretability, uncertainty qualification and inspection prioritization are as important as raw classification accuracy.

Association of care fragmentation with financial and clinical outcomes of transcatheter aortic valve replacement in the US: A retrospective cohort study

Purpose Care fragmentation (CF), defined as readmission to a non-index facility, has historically been linked with adverse clinical and financial outcomes for a variety of cardiac procedures. However, its impact on patients undergoing transcatheter aortic valve replacement (TAVR) remains understudied. Methods This retrospective study investigated the prevalence and impact of CF on outcomes of patients undergoing TAVR. All adults (≥18 years) undergoing an isolated TAVR procedure, who were readmitted within 90 days, were tabulated using the Nationwide Readmissions Database from 2016 to 2021. A 90-day readmission window was chosen in accordance with prior literature on readmission. Patients treated at a non-index facility were categorized into the CF cohort. Multivariable logistic models were developed to characterize the association of care fragmentation status with perioperative complications and resource utilization; candidate variables were selected using elastic net regularization to minimize variable collinearity. Results Of an estimated 55,891 patients who were readmitted within 90 days, 37.4% were treated at a non-index facility. While TAVR utilization more than doubled, both the readmission and care fragmentation rates fell during the study period. Following adjustment, CF status was associated with respiratory (AOR [Adjusted Odds Ratio] 1.20, 95% CI [Confidence Interval] 1.11–1.29, p < 0.001), gastrointestinal (AOR 1.21, 95% CI 1.05–1.38, p = 0.008), and infectious complications (AOR 1.17, 95% CI 1.10–1.25, p < 0.001) at readmission. Furthermore, CF was linked with greater risk of non-home discharge (AOR: 1.13 95% CI [1.03–1.25], P = 0.01). Conclusions As TAVR utilization continues to expand nationwide, further efforts to enhance care coordination and streamline information sharing are warranted to mitigate the burden of care fragmentation.

Optimal capacitor value calculation for self excited induction generators using hybrid grey wolf differential evolution optimization with experimental validation

Continuation of beta-blockers during prolonged dobutamine infusion in heart transplant–prioritised patients: A competing-risk analysis

Background Patients awaiting heart transplantation (HT) frequently require prolonged continuous dobutamine infusion. Whether oral beta-blocker (BB) therapy should be maintained during this period remains controversial and poorly studied, particularly in the context of prolonged concomitant use. The impact of BB continuation on waiting-list outcomes in this specific scenario is unknown. Methods We conducted a retrospective single-centre cohort study including adult patients listed for HT who received ≥ 30 consecutive days of concomitant oral BB therapy and continuous intravenous dobutamine between January 2020 and December 2023. The index date was defined as the first day after completion of this 30-day period, identifying patients who had already demonstrated sustained haemodynamic tolerance to the combination. Patients were classified according to BB continuation or suspension after the index date. Competing-risk analyses were performed using inverse probability of treatment weighting (IPTW), Aalen–Johansen cumulative incidence functions, and Fine–Gray regression. Robustness was assessed through nonparametric bootstrapping (1,500 replications). Results Among 53 eligible patients (mean age 50.8 ± 11.9 years; 64% male), 29 underwent HT and 16 died before transplantation. After IPTW adjustment, BB continuation was associated with a higher cumulative incidence of HT at ≥ 270 days (61.1% vs 14.3%; p < 0.001) and a lower cumulative incidence of pre-transplant death (13.3% vs 68.7%; p < 0.001). Fine–Gray regression confirmed these associations: HT sHR 8.79 (95% bootstrap CI 2.66–54.31) and pre-transplant mortality sHR 0.077 (95% bootstrap CI 0.014–0.192). Results were consistent across four pre-specified sensitivity analyses. Conclusions This study evaluates outcomes in patients sustaining oral beta-blocker therapy during prolonged dobutamine infusion while awaiting heart transplantation, a clinical scenario with limited available data. BB continuation was associated with a higher probability of transplantation, with consistent direction across pre-specified sensitivity analyses. Given the small sample size and the potential for residual confounding by indication, these findings should be interpreted with caution and are best viewed as hypothesis-generating. They may help inform future research, particularly prospective studies in this setting.