Search Results (13,039)

Background: Herpes zoster (HZ) poses significant risks to immunocompromised individuals, particularly cancer patients receiving systemic therapies. The recombinant zoster vaccine (RZV, Shingrix^®) provides strong and durable protection against HZ and its complications. Nevertheless, vaccination coverage remains low, mainly due to limited awareness among patients and healthcare providers and logistical barriers to vaccine access and delivery. Materials and Methods: We conducted a single-center quality improvement (QI) project to enhance RZV uptake among oncology outpatients receiving systemic therapy. Following the Plan–Do–Study–Act (PDSA) model, baseline HZ vaccination coverage was assessed, and an in-house vaccination campaign was implemented. Vaccination rates were monitored every two months over a 14-month period. Results: At baseline, only 5.4% (24/446) of patients had received RZV. After 14 months, 365 patients were evaluated for vaccination: 200 (55%) were vaccinated, 134 (37%) were ineligible, and 31 (8%) refused RZV. The overall vaccination rate increased from 5.4% to 44%. Reported adverse events were mild and primarily local reactions, confirming the vaccine’s favorable safety profile in this population. Conclusions: This real-world QI initiative demonstrates that an in-house vaccination strategy embedded within oncology services can substantially improve RZV coverage and patient engagement. The approach highlights the key role of oncology teams in leading preventive interventions for immunocompromised patients. By integrating vaccination into routine cancer care, institutions can overcome traditional organizational barriers and align with current ASCO and ESMO recommendations for comprehensive patient protection. Full article

Objectives: This cross-sectional case–control study, conducted at the Department of Physiotherapy, Academy of Physical Education, in Katowice, between July 2022 and April 2023, aimed to assess the overall health status (OHS) of patients with mild-to-moderate cubital tunnel syndrome (CuTS) compared with a healthy control population. Methods: The SF-36 questionnaire was used to evaluate the following domains: physical functioning (PF), role limitations due to physical health problems (RP), bodily pain (BP), general health (GH), vitality (VT), social functioning (SF), role limitations due to emotional problems (RE), and mental health (MH). Results: A total of 152 participants were enrolled in the study. The CuTS and control groups consisted of 82 and 70 individuals, respectively. The CuTS group had significantly lower scores in most domains when compared with the control group; however, no significant difference was observed in the BP domain. Notably, the VT domain showed a statistically significant difference in favor of the CuTS group, indicating slightly higher perceived vitality despite functional limitations. Conclusions: Mild and moderate forms of CuTS are associated with significant deterioration in general health, encompassing both physical and psychological components, as assessed by the SF-36 questionnaire; however, no significant difference was observed in the BP domain, suggesting that the burden of CuTS may manifest more strongly in functional, psychosocial, and mental health aspects rather than in generalized pain perception. These findings highlight the importance of a comprehensive evaluation and management strategy that addresses both physical and psychosocial dimensions of patient care. Full article

Mitigating ground vibrations from sources like vehicles and construction operations poses significant challenges, often relying on computationally intensive numerical methods such as Finite Element Methods (FEM) or Boundary Element Methods (BEM) for analysis. This study addresses these limitations by developing and evaluating Machine Learning (ML) methodologies for the rapid and accurate prediction of Insertion Loss (IL), a critical parameter for assessing the effectiveness of open trenches as vibration barriers. A comprehensive database was systematically generated through high-fidelity numerical simulations, capturing a wide range of geometric, elastic, and physical configurations of a stratified geotechnical system. Three distinct ML strategies—Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forests (RF)—were initially assessed for their predictive capabilities. Subsequently, a Meta-RF stacking ensemble model was developed, integrating the predictions of these base methods. Model performance was rigorously evaluated using complementary statistical metrics (RMSE, MAE, NMAE, R), substantiated by in-depth statistical analyses (normality tests, Bootstrap confidence intervals, Wilcoxon tests) and an analysis of input parameter sensitivity. The results clearly demonstrate the high efficacy of Machine Learning (ML) in accurately predicting IL across diverse, realistic scenarios. While all models performed strongly, the RF and the Meta-RF stacking ensemble models consistently emerged as the most robust and accurate predictors. They exhibited superior generalization capabilities and effectively mitigated the inherent biases found in the ANN and SVM models. This work is intended to function as a proof-of-concept and offers promising avenues for overcoming the significant computational costs associated with traditional simulation methods, thereby enabling rapid design optimization and real-time assessment of vibration mitigation measures in geotechnical engineering. Full article

Background: Noonan syndrome (NS) is a genetically heterogeneous condition within the RASopathies spectrum, with distinctive craniofacial features, congenital heart defects, short stature, and variably present developmental delay. Most cases result from variants in genes regulating the RAS/MAPK pathway, with PTPN11 variants being the most frequent; the c.922A>G substitution being among the most commonly reported. Methods: This pilot study analyzed clinical and partial genetic features of NS in a cohort from Transylvania, evaluated in the Children’s Emergency Clinical Hospital in Cluj-Napoca. Thirty-one patients fulfilling the Van der Burgt diagnostic criteria (twenty-two males, nine females) were included. Clinical data were systematically reviewed, and targeted molecular testing for the PTPN11 c.922A>G variant was performed. Results: Congenital heart defects were highly prevalent, with pulmonary stenosis representing the most frequent anomaly (54.8%). Craniofacial dysmorphism was observed in 76.7% of cases, cryptorchidism in 50% of the males, and short stature below the third percentile was described in 77.4% of patients. Genetic screening identified the PTPN11 c.922A>G variant in two individuals (6.45%). Additional diagnoses included Williams–Beuren syndrome and a 17q11.2 deletion consistent with Neurofibromatosis–Noonan syndrome, underscoring the clinical and genetic heterogeneity of the cohort. Comparison with international reports highlighted variability in phenotype and variant frequency. Future research directions include Sanger sequencing of key PTPN11 exons and the application of next-generation sequencing targeting all RAS pathway genes. Conclusions: This is the first Romanian cohort study on patients with a clinical suspicion of NS, providing insight into their evaluation. The findings reinforce the need for comprehensive molecular approaches, facilitating diagnostic precision and counseling strategies. Full article

Background: In vivo confocal microscopy (IVCM) provides high-resolution corneal imaging that may enhance preoperative and postoperative assessment in refractive surgery. This pilot study aimed to evaluate the diagnostic utility of IVCM in identifying subclinical corneal abnormalities that could influence surgical qualification and outcomes. Methods: A total of 7 patients (3 males, 4 females; mean age 48.8 ± 14.5 years) undergoing qualification or follow-up for refractive surgery were prospectively examined between May 2021 and March 2025. Each participant underwent a comprehensive ophthalmic evaluation, including slit-lamp biomicroscopy, corneal topography, anterior segment optical coherence tomography (AS-OCT), and IVCM using the Heidelberg Retina Tomograph II with Rostock Cornea Module. Patients with prior ocular surgery, active infection, or systemic corneal disease were excluded. Results: IVCM revealed subtle epithelial, stromal, and endothelial abnormalities undetectable by conventional methods. Findings such as Thygeson’s keratitis, pre-Descemet’s dystrophy, and subclinical herpes simplex keratitis led to modifications of surgical plans or disqualification in selected cases. The technique also aided postoperative evaluation of epithelial–stromal interface disorders. Conclusions: IVCM proved to be a valuable adjunct in detecting subclinical corneal pathology, refining patient selection, and improving safety in refractive surgery. Larger multicenter studies are warranted to validate its clinical role and define standardized indications for preoperative screening. Full article

Cooperative navigation is a fundamental enabling technology for unlocking the full potential of Unmanned Underwater Vehicle (UUV) clusters in GNSS-denied environments. However, the severe constraints of the underwater acoustic channel, such as high latency, low bandwidth, and non-Gaussian noise, pose significant challenges to designing robust and efficient state estimation and information fusion algorithms. While numerous surveys have cataloged the available techniques, they have remained largely descriptive, lacking a rigorous, quantitative comparison of their performance trade-offs under realistic conditions. This paper provides a comprehensive and critical review that moves beyond qualitative descriptions to establish a novel quantitative comparison framework. Through a standardized benchmark scenario, we provide the first data-driven, comparative analysis of key frontier algorithms—from recursive filters like the Maximum Correntropy Kalman Filter (MCC-KF) to batch optimization methods like Factor Graph Optimization (FGO)—evaluating them across critical metrics including accuracy, computational complexity, communication load, and robustness. Our results empirically reveal the fundamental performance gaps and trade-offs, offering actionable insights for system design. Furthermore, this paper provides in-depth technical analyses of advanced topics, including distributed fusion architectures, intelligent strategies like Deep Reinforcement Learning (DRL), and the unique challenges of navigating in extreme environments such as the polar regions. Finally, leveraging the insights derived from our quantitative analysis, we propose a structured, data-driven research roadmap to systematically guide future investigations in this critical domain. Full article

Global crop losses of 20–40% continue because traditional plant assessment methods are either invasive, damaging plant tissues, or reactive, detecting stress only after visible symptoms. Recent developments have remained fragmented, focusing on single modalities, individual organs, or limited frequency ranges. This study developed a unified bioelectrical sensor system capable of non-invasive, multimodal, multiscale, and integrative assessment by integrating capabilities that existing methods address only separately. The system combines spectroscopy and tomography within a single platform, enabling simultaneous evaluation of multiple organs. Unlike approaches confined to narrow frequencies, it captures complete physiological responses across scales. Validation on strawberry (Fragaria × ananassa ‘Sweet Charlie’) demonstrated comprehensive multi-organ assessment: 98.3% accuracy for fruit categorization, 95.8% for leaf water status, and 88.2% for stem productivity. Tomographic performance reached 2.6–2.8 mm resolution for 3D root mapping and 2.8–3.0 mm for 2D postharvest fruit sorting. Correlations with reference metrics were used exclusively for validation, confirming that the extracted features reflect genuine physiological variations. Importantly, the system detects stress before visible symptoms, enabling intervention within the reversible window. By unifying spectroscopy and tomography with complete frequency coverage and multi-organ capability, this platform overcomes existing fragmentation and establishes a foundation for proactive, comprehensive plant monitoring essential for sustainable agriculture. Full article

Background/Objectives: Utilization of polyetheretherketone (PEEK) cages for spinal fusion has surged in the U.S., yet comprehensive comparisons evaluating its postoperative effectiveness with alternative materials remain limited. This systematic review investigates the efficacy of PEEK cages against traditional fusion materials across various surgery types, elucidating PEEK’s impact on fusion rates, postoperative outcomes, and long-term success. Methods: A systematic search of PubMed, CINAHL, Scopus, Embase, and Web of Science was conducted through 14 October 2024. Included studies were randomized controlled trials (RCTs) comparing PEEK cages with titanium, silicon nitride, and metal-coated PEEK cages for anterior cervical discectomy and fusion (ACDF), posterior lumbar interbody fusion (PLIF), and transforaminal lumbar interbody fusion (TLIF). Article quality was assessed using GRADE criteria. Results: From 288 initially screened articles, 25 RCTs involving 2046 patients (mean follow-up 23.1 ± 18.2 months) met inclusion criteria and were determined as moderate (n = 21) or high (n = 4) quality. Fusion rates by cage material for PEEK (n = 1041), Ti-PEEK (n = 291), and titanium (n = 53) were 85.63 ± 18.00%, 80.05 ± 19.9%, and 92.75 ± 11.31%, respectively. In ACDF, titanium cages achieved higher fusion rates than PEEK (100% vs. 94%). In PLIF and TLIF, coated PEEK outperformed uncoated PEEK (75% vs. 71% and 94% vs. 84%, respectively). Uncoated PEEK achieved fusion rates of 94.04 ± 5.04% for ACDF, 71.21 ± 21.93% for PLIF, and 83.50 ± 24.66% for TLIF, with titanium outperforming PEEK in early fusion outcomes. Coated PEEK demonstrated potential improvements in fusion rates over uncoated PEEK in PLIFs and TLIFs. Conclusions: Selection of cage material for spinal fusions should be tailored to surgical requirements and patient needs. While titanium and PEEK are effective, their performance varies across contexts. New materials and surface modifications may enhance these outcomes further, warranting future research in long-term studies and development of novel materials. These findings can help surgeons choose cage materials according to procedure type, patient characteristics, and imaging needs. Full article

Genetic variability in terrestrial mammals is essential for understanding population and evolutionary dynamics, as well as for establishing effective strategies in conservation biology. This comprehensive review aimed to critically analyze invasive and non-invasive techniques used to assess genetic variability in wild terrestrial mammals. Using the PICO (Population, Intervention, Comparison, Outcome) format and following PRISMA guidelines, a comprehensive literature search was conducted in Web of Science, Scopus and Science Direct databases, including articles published in English from January 2015 to April 2025. Thirty-one experimental studies were selected that met specific criteria related to genetic evaluation using invasive (direct blood or tissue collection) and non-invasive (stool, hair and saliva collection) techniques. The results indicate that invasive techniques provide samples of high genetic quality, albeit with important ethical and animal welfare considerations. In contrast, non-invasive techniques offer less disruptive methods, although they present significant challenges in terms of quantity and purity of DNA obtained, potentially affecting the accuracy and confidence of genetic analysis. Detailed analysis of selected studies showed diverse patterns of heterozygosity and inbreeding coefficients between different taxonomic orders (Carnivora, Artiodactyla, Proboscidea, Primates and Rodentia). In addition, the main anthropogenic threats and current conservation strategies implemented in different species were identified. An overall genetic variability ranging from high to moderate was observed, with large species being more vulnerable to genetic reduction due to changes in habitat and human activities. Rather than a static comparison, our synthesis traces a clear methodological arc from small short tandem repeats (STR, or microsatellites) panels towards SNP-based approaches enabled by next-generation sequencing, including reduced representation (ddRAD), amplicon panels (GT-seq), and hybridisation capture tailored to degraded DNA from hair, faeces, and environmental substrates. Over 2015–2025, study designs shifted from presence/absence and coarse diversity estimates to robust inference of relatedness, assignment, effective population size, and gene flow using hundreds–thousands of SNPs and genotype-likelihood frameworks tolerant of allelic dropout and low coverage. Laboratory practice converged on multi-tube replication, synthetic blocking oligos, and capture-based enrichment; bioinformatics adopted probabilistic genotype calling, error-aware filtering, and replication-based consensus. This review provides a solid basis for optimizing genetic sampling methods, allowing for more ethical and efficient studies. Furthermore, it contributes to strengthening conservation strategies by underlining the importance of adapting the sampling method to the biological and ecological particularities of each species studied. Ultimately, these findings can significantly improve genetic conservation decision-making, benefiting the sustainability and resilience of wild land mammal populations. Full article

A field experiment was conducted on 16-year-old ‘Wen 185’ walnut trees in Aksu, Southern Xinjiang, to identify optimal water and fertilizer management under subsurface drip irrigation. Four irrigation levels were established: 75% ET_c (W1), 100% ET_c (W2), 125% ET_c (W3), and 150% ET_c (W4). These were combined with three fertilizer levels: N 270, P 240, K 300 kg ha⁻¹ (F1), N 360, P 320, K 400 kg ha⁻¹ (F2), and N 450, P 400, K 500 kg ha⁻¹ (F3). This resulted in a total of 12 treatments. This study assessed the impact of different water and fertilizer treatments on walnut growth dynamics, yield, fruit quality, water and fertilizer use efficiency, and soil nitrate residue. Principal component analysis (PCA) was used to construct comprehensive growth and photosynthesis indices (CGI and CPI). Parameters significantly correlated with yield and quality were then screened via Pearson analysis, and a game theory-based combination weighting method was adopted to determine weights for integrating six categories of indicators: growth, photosynthesis, yield, quality, resource use efficiency, and environmental impact. A coupled TOPSIS-GRA model was developed for comprehensive evaluation. Furthermore, binary quadratic regression was employed to optimize the application ranges of water and fertilizer. The results showed that the W2F2 treatment achieved the highest rank by synergistically enhancing growth, photosynthetic performance, yield, and quality. This treatment also maintained high water use efficiency (WUE) and partial factor productivity of fertilizer (PFP) and effectively reduced nitrate accumulation in deep soil layers. The CGI and CPI, derived from PCA, effectively quantified phenological growth and photosynthetic characteristics. Correlation analysis identified seven core parameters, among which IV-CPI correlated most strongly with yield. In contrast, II-CPI was more closely associated with increased single-fruit weight and reduced tannin content. Within the comprehensive evaluation system that used game theory-based combination weighting, yield received the highest weight (0.215), while IV-CPI was assigned the lowest (0.011). The TOPSIS-GRA coupled model identified the W2F2 treatment as the highest-ranked. Furthermore, regression optimization determined the optimal total seasonal application ranges to be 5869.94–6519.81 m³ ha⁻¹ for irrigation and 975.54–1107.49 kg ha⁻¹ for fertilization. The coupled TOPSIS-GRA model enabled a balanced assessment of the objectives: high yield, superior quality, resource use efficiency, and environmental sustainability. Thus, it provides a theoretical foundation and practical guidance for enhancing the productivity and sustainability of subsurface drip-irrigated walnut orchards in Southern Xinjiang. Full article

The ecological protection and high-quality development of the Yellow River Basin is a major national strategy in China. The Yellow River “Ji” Bay is an important part of the basin. This study evaluates the comprehensive risk of the water–energy–food–ecology–carbon (WEFEC) complex system within the “Ji” Bay. Using 2004–2023 panel data from nineteen regional cities, this study develops a 24-indicator WEFEC index system that assesses reliability, synergy, and resilience. A comprehensive evaluation method based on the game theory–cloud model is employed to determine the risk levels. The study results show the following: (1) the multi-year average comprehensive risk of the WEFEC complex system in the “Ji” Bay from 2004 to 2023 was at a high alert level; (2) the overall synergy of the “Ji” Bay was moderate; (3) spatially, the number of cities in extreme and high alert states decreased, whereas the number of cities in no alert and light alert states increased; and (4) indicators such as per capita water resources, water production modulus, and water area ratio are the main factors restricting the comprehensive risk of the WEFEC complex system. Based on these findings, this paper proposes policy recommendations using the following three aspects: criterion layers, risk factors, and different regions. Full article

Background/Objectives: The prevailing paradigm in ophthalmic AI involves siloed, single-disease models, which fails to address the complexity of differential diagnosis in clinical practice. This study aimed to develop and validate a unified deep learning framework for the automated multi-class classification of a wide spectrum of retinal pathologies from fundus photographs, moving beyond the single-disease paradigm to create a comprehensive screening tool. Methods: A publicly available dataset was manually curated by an ophthalmologist, resulting in 1841 images across nine classes, including Diabetic Retinopathy, Glaucoma, and Healthy retinas. After extensive data augmentation to mitigate class imbalance, three pre-trained CNN architectures (ResNet-152, EfficientNetV2, and a YOLOv11-based classifier) were comparatively evaluated. The models were trained using transfer learning and their performance was assessed on an independent test set using accuracy, macro-averaged F1-score, and Area Under the Curve (AUC). Results: The YOLOv11-based classifier demonstrated superior performance over the other architectures on the validation set. On the final independent test set, it achieved a robust overall accuracy of 0.861 and a macro-averaged F1-score of 0.861. The model yielded a validation set AUC of 0.961, which was statistically superior to both ResNet-152 (p < 0.001) and EfficientNetV2 (p < 0.01) as confirmed by the DeLong test. Conclusions: A unified deep learning framework, leveraging a YOLOv11 backbone, can accurately classify nine distinct retinal conditions from a single fundus photograph. This holistic approach moves beyond the limitations of single-disease algorithms, offering considerable promise as a comprehensive AI-driven screening tool to augment clinical decision-making and enhance diagnostic efficiency in ophthalmology. Full article

Live fuel moisture content (LFMC) is a critical variable influencing wildfire behavior, ignition potential, and suppression difficulty, yet it remains challenging to monitor consistently across landscapes due to sparse field observations, rapid temporal changes, and vegetation heterogeneity. This study presents a comprehensive review of satellite-based approaches for estimating LFMC, with emphasis on methods applicable to New Zealand, where wildfire risk is increasing due to climate change. We assess the suitability of different remote sensing data sources, including multispectral, thermal, and microwave sensors, and evaluate their integration for characterizing both LFMC and fuel types. Particular attention is given to the trade-offs between data resolution, revisit frequency, and spectral sensitivity. As knowledge of fuel type and structure is critical for understanding wildfire behavior and LFMC, the review also outlines key limitations in existing land cover products for fuel classification and highlights opportunities for improving fuel mapping using remotely sensed data. This review lays the groundwork for the development of an operational LFMC prediction system in New Zealand, with broader relevance to fire-prone regions globally. Such a system would support real-time wildfire risk assessment and enhance decision-making in fire management and emergency response. Full article

With the comprehensive digitalization and electrification of aircraft, electromechanical actuation systems (EAS) have been increasingly applied. However, EAS are affected by various nonlinear factors, such as friction and mechanical backlash, which can compromise system stability and control accuracy, thereby reducing the operational lifespan of the EAS. This study focuses on these two nonlinear factors and proposes a hybrid control approach to mitigate their effects. In the speed loop of the EAS, a Super-Twisting sliding mode controller combined with a generalized proportional–integral observer (GPIO) is designed, while in the position loop, a hybrid controller integrating a radial basis function (RBF) neural network with sliding mode control is implemented. Leveraging the advantages of numerical analysis in SIMULINK and dynamic simulation in ADAMS, a co-simulation framework is established to evaluate the hybrid control algorithm under nonlinear effects. Furthermore, a control test bench for the control surface transmission system is constructed to analyze the dynamic and static performance of the system under different control strategies and input commands. The experimental results show that, compared with the PID control, the hybrid control method reduces the steady-state error and vibration amplitude of the step response displacement by 51% and 75%, respectively, and decreases the amplitude of speed fluctuations by 75%. For the sinusoidal response, the displacement lag is reduced by 76%, and the amplitude of speed fluctuations is reduced by 50%. Full article

Background: Chronic skin ulcers are characterized by an impaired and delayed wound healing process, posing a major economic and healthcare burden. These multifactorial conditions are influenced by both biological, clinical and psychosocial factors. The aim of our cross-sectional study was to investigate the influence of psychosocial and lifestyle factors—specifically adherence to the Mediterranean diet, emotional health, sleep quality and demographic characteristics—on physical symptoms and clinical severity in patients with skin ulcers, using a multidisciplinary approach to identify key predictors of disease burden. Methods: A cross-sectional analysis was conducted on patients with skin ulcers, using data from a monocentric pathology registry. Collected variables included gender, age, dietary habits (specifically, adherence to the Mediterranean diet), sleep disturbances, educational level, anxiety and depressive symptoms, Physician Global Assessment (PGA), Patient Global Assessment (PtGA), and Skindex-17 (a dermatology-specific quality of life measure). Hierarchic multivariate linear regression models were applied to identify predictors of physical symptoms and clinical severity, while simultaneously controlling for potential confounders. Results: Older age, poorer adherence to the Mediterranean diet, and elevated anxiety levels emerged as the strongest predictors of worse physical symptoms, as measured by the Skindex-17. Male sex and more severe depressive symptoms were significantly associated with higher PGA scores. Our data also suggest older age and poorer Mediterranean diet adherence to influence clinical severity. Lastly, sleep disturbances were also found to correlate with patient-reported severity. Conclusions: Our study underscores the impact of psychosocial and behavioral/lifestyle factors on the clinical burden of skin ulcers through a comprehensive multidisciplinary approach. In particular, our data indicate that dietary patterns and emotional health appear to shape both symptom perception and clinical evaluation, emphasizing the need for holistic management strategies. Full article