Search Results (1,730)

Background: Head and neck cancers (HNCs) represent a global health burden, with high morbidity and mortality largely driven by late-stage diagnosis and heterogeneous clinical outcomes. Reliable biomarkers are needed to improve early detection, prognostic stratification, and therapeutic decision-making. This study evaluates the diagnostic and prognostic value of chemokines in HNCs and identifies candidates with clinical relevance. Methods: A comprehensive literature search was conducted on 28 March 2026 across PubMed, Embase, Scopus, Web of Science, and EBSCO. Observational studies involving patients or biological samples with confirmed HNCs were included if they evaluated chemokines as diagnostic or prognostic biomarkers. Risk of bias was assessed using the Newcastle–Ottawa Scale (NOS) for prognostic studies and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool for diagnostic studies. Meta-analyses were performed for chemokines evaluated in ≥3 studies using the inverse-variance heterogeneity model. Results: Forty-four studies encompassing 7294 participants were included. Prognostic findings were heterogeneous across biomarkers. MIP-3α demonstrated consistently significant associations with poorer survival outcomes. In contrast, IL-8, CXCL10, and CXCR4 showed inconsistent or predominantly non-significant associations with overall survival (OS), disease-free survival (DFS), and locoregional control (LRC). For diagnostic performance, IL-8, in saliva, demonstrated relatively high sensitivity and specificity for oral squamous cell carcinoma. Chemerin showed high diagnostic accuracy, although evidence remains limited. Conclusions: Certain chemokines show potential as diagnostic and prognostic biomarkers in HNCs. However, heterogeneity across tumor types, samples, and methodologies limits evidence. Larger, well-designed studies are needed to validate their clinical utility in risk stratification and personalized management of HNCs. Full article

The large-scale operation of multiple fixed-wing unmanned aerial vehicles (UAVs) in shared airspace requires efficient flight conflict detection and resolution to ensure aviation safety. However, existing research predominantly lacks collaborative optimization of multi-dimensional maneuver recommendations and struggles with dynamic priority allocation in complex multi-UAV scenarios, leaving a critical gap in the field. To bridge this gap, this paper proposes a Complex Network-Based Multi-UAV Conflict Resolution (NCR) method, which first constructs a three-dimensional (3D) flight conflict detection and resolution model for fixed-wing UAVs. The core innovation lies in mapping dynamic multi-UAV conflict scenarios into a flight conflict network, where UAVs serve as nodes and conflict urgencies act as edge weights. By calculating network and node robustness, the method accurately identifies key UAVs requiring immediate maneuver. Subsequently, taking the minimum variation in the velocity vector as the core objective, NCR iteratively searches for optimal resolution recommendations for these key UAVs using an improved fitness function until the conflict network collapses. Simulation and comparative experiments in 3D airspace, including evaluations against serial-based resolution, random-recommendation resolution, and a classical reactive baseline, demonstrate that NCR efficiently resolves multi-UAV conflicts with minimal trajectory deviations and fewer maneuvering UAVs. Furthermore, a macro-micro bi-level validation architecture based on a six-degree-of-freedom (6-DOF) aerodynamic platform is introduced to verify the physical executability of the proposed strategies. Results demonstrate that by incorporating a dynamic aerodynamic compensation margin, the inevitable trajectory tracking deviations caused by system inertia are enveloped within the safety threshold, ensuring absolute flight safety in engineering practice. Notably, as conflict complexity increases, NCR exhibits prominent advantages in reducing velocity variation costs, minimizing the number of maneuvering UAVs, and avoiding unnecessary trajectory deviations. Full article

Indoor environments often contain numerous areas with sparse structural features, such as long corridors, large atriums, and glass curtain walls, and other scenarios. These conditions can lead to difficulties in loop closure detection and accumulated positioning errors, resulting in localization drift or even mapping failure during map construction. This paper proposes an indoor mapping algorithm called IS-LEGO-LOAM that integrates tightly coupled LiDAR-IMU fusion and Scan Context. A tightly coupled LiDAR-IMU odometry is constructed, and an adaptive covariance matrix is designed to solve the problems of abnormal LiDAR echoes and insufficient effective feature extraction caused by sparse indoor feature points. By introducing the Scan Context global descriptor and adopting the strategies of vector nearest neighbor search and similarity score matching, the drift problem in large-scale scenes is alleviated. Finally, validation is performed on the KITTI dataset and in real-world scenarios, respectively. Experiments show that the improved IS-LEGO-LOAM achieves superior mapping performance. Full article

Background: Tourette syndrome (TS) is a chronic neurodevelopmental disorder with a significant rate of patients remaining refractory to standard treatments. Refractory TS is defined as the persistence of clinically significant tics causing functional impairment despite adequate trials of both behavioral therapy and at least two generally accepted pharmacological treatments from different classes, administered at appropriate doses and durations. This systematic review synthesizes the current evidence on alternative pharmacological and non-pharmacological interventions for treatment-resistant TS. Methods: The review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. PubMed, Embase, and Scopus were searched for studies published from 2010 onward. Eligible publications included randomized and non-randomized clinical studies, and case reports evaluating alternative interventions in refractory TS, excluding deep brain stimulation. The primary outcome measures included validated scales assessing tic severity and functional impairment, with available data demonstrating reductions in tic severity, including clinically meaningful improvements reported in several studies. Thirteen studies met the inclusion criteria, comprising five clinical studies (including three randomized trials) and eight case reports. Heterogeneity across studies was primarily driven by differences in study design, patient populations, and variability in intervention types, protocols, and outcome assessments. Results: Investigated interventions included cannabinoids, valproic acid, deep transcranial magnetic stimulation, neurofeedback, biofeedback, electroconvulsive therapy, and ablative neurosurgical procedures. Cannabinoid-based treatments showed potential reductions in tic severity; however, results were inconsistent and often not statistically significant, with evidence largely derived from small or uncontrolled studies. Evidence for non-pharmacological approaches was limited and largely derived from individual cases, with some modalities showing potential benefit in specific subgroups, such as patients with comorbid obsessive-compulsive disorder. Conclusions: Overall, alternative interventions may offer therapeutic value for selected individuals with treatment-resistant TS, with the greatest data regarding cannabinoid use; however, the current evidence base remains heterogeneous and methodologically constrained. Deep transcranial magnetic stimulation might be beneficial in a specific patient population. Larger, well-controlled studies are required to clarify efficacy, safety, and treatment response. Full article

Accurate trajectory tracking is fundamental to the autonomous operation of unmanned aerial vehicles (UAVs) in complex tasks. While proximal policy optimization (PPO) has shown strong potential in UAV control, its performance is highly sensitive to hyperparameter configuration, and manual tuning is time-consuming due to complex interparameter coupling. This paper proposes CCO–PPO, a framework integrating the cuckoo catfish optimizer (CCO) with PPO for automatic hyperparameter optimization in UAV trajectory tracking. The problem is formulated as a Markov decision process with a 20-dimensional state space, and the CCO performs offline search over a four-dimensional hyperparameter space. Evaluated across seven test environments covering diverse trajectory geometries, wind disturbances, sensor noise, and large-scale scenarios, CCO–PPO achieves the lowest tracking error in all cases. Performance gains over baseline PPO increase monotonically with task complexity, reaching 18.8% under combined wind disturbance and sensor noise, with statistically significant advantages in 85.7% of pairwise comparisons against baseline PPO, SAC, and TD3. Ablation studies confirm that joint optimization of all four hyperparameters is essential under high-disturbance conditions, and comparisons with Bayesian optimization validate the CCO’s superior cross-seed stability. These results demonstrate that metaheuristic hyperparameter optimization substantially enhances policy robustness in high-disturbance UAV trajectory tracking scenarios. Full article

Background: Continuous positive airway pressure (CPAP) is the most effective treatment for obstructive sleep apnoea (OSA). However, CPAP adherence in randomized controlled trials (RCTs) is frequently inadequate, potentially leading to an underestimation of the therapy’s true effect on relevant outcomes. The aim was to identify patient and study characteristics that predict adherence to CPAP therapy in RCTs. Methods: PubMed and the existing meta-analyses were searched (1984 to 31 December 2024). A study-level meta-analysis of RCTs comparing CPAP with inactive control in patients with OSA was conducted. Meta-regressions and subgroup analyses (<4 h vs. ≥5 h usage) were undertaken to identify the predictors of CPAP adherence. Risk-of-bias was assessed using the Cochrane RoB-2 tool. Results: In 136 RCTs reporting on CPAP use, including 8827 patients with OSA (55 [49.5–59.8] years, 77.4 [61.2–89.2]% male, BMI 31 [28.9–33.2] kg/m², Epworth Sleepiness Scale (ESS) 10.0 ± 2.8, apnoea–hypopnoea-index (AHI) 35.7 ± 13.4/h), mean nocturnal CPAP use was 4.5 ± 1 h. CPAP use of ≥4 h, ≥5 h, and ≥6 h per night was observed in 71.3%, 34.1%, and 7.8% of RCTs, respectively. Higher baseline AHI was the strongest predictor of longer CPAP use in meta-regressions (p < 0.001, β = 0.02, 95% CI 0.01–0.04). Baseline AHI was also significantly higher (40.3 ± 12.8 vs. 29.9 ± 12.6) in the ≥5 h vs. <4 h subgroup (p < 0.01, large effect size d = 0.84). A higher nightly CPAP usage was more likely in smaller (p < 0.05, d = 0.45) and single-centre trials (p < 0.05, h = 0.52). Sex distribution, age, BMI, ESS, and follow-up had no significant effect on nightly CPAP use. Conclusions: Higher baseline AHI independently predicted longer CPAP use in RCTs, while sleepiness and demographics did not. This study was registered at PROSPERO (CRD420250653394) and received no external funding. Full article

Accurate reservoir dam detection in arid regions is challenging because of spectral similarity between dams and surrounding backgrounds, indistinct boundaries, and substantial target-scale variation. To address these issues, this study proposes a deep learning object detection with multi-conditional constraint assistance (DLOD&MCCA) framework that combines a dual deep enhancement YOLO network (DDE-YOLO) with a multi-conditional constraint assistance (MCCA) strategy. In DDE-YOLO, visible (VIS) and near-infrared (NIR) imagery are fused to enhance cross-spectral discrimination, while task-oriented architectural refinements improve the representation of dam targets with diverse scales and structural characteristics. Meanwhile, the MCCA strategy constrains the search space to geographically plausible candidate regions, thereby reducing background interference and improving detection efficiency. Experiments conducted on the self-constructed S2-Dam dataset and the public DIOR dataset show that DDE-YOLO achieves mAP50 values of 92.8% and 76.2%, respectively, outperforming existing state-of-the-art (SOTA) methods. Furthermore, regional-scale dam mapping in Xinjiang achieved an accuracy of over 95%, demonstrating the effectiveness and practical applicability of the proposed framework for large-scale reservoir dam detection in arid environments. Full article

Background: Metabolic dysregulation is increasingly recognized as a contributor to carcinogenesis; however, the role of circulating lipid traits in ovarian cancer remains unclear. Methods: A systematic review and meta-analysis were conducted following PRISMA 2020 guidelines. PubMed, Web of Science, Scopus, and Embase were searched from inception to March 2026. Observational studies evaluating triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and total cholesterol (TC) in relation to ovarian cancer risk were included. Random-effects models were used to pool relative risks (RRs). Robustness was assessed via sensitivity analyses, influence diagnostics, and multiverse analysis. Mendelian randomization (MR) evidence was integrated for causal inference. Results: Six observational studies were included in the meta-analysis. Elevated triglyceride levels were associated with increased ovarian cancer risk, while HDL-C showed a modest inverse association. LDL-C and total cholesterol were not significantly associated with risk. Sensitivity analyses excluding early follow-up strengthened the triglyceride association. MR analyses supported a potential causal role for triglycerides but not for HDL-C. Conclusions: Circulating triglycerides may represent a metabolically relevant risk factor for ovarian cancer. Further large-scale prospective and mechanistic studies are warranted. Full article

Campylobacter is the primary bacterial cause of foodborne gastroenteritis globally. While international standards recommend a tiered approach for detection, emerging evidence suggests that selective protocols may introduce species-specific recovery biases. This systematic review and meta-analysis evaluated the diagnostic performance of established culture-based detection protocols across diverse food matrices. Following PRISMA 2020 guidelines, we searched multiple databases for studies reporting 2 × 2 diagnostic accuracy data through October 2024. Ten studies comprising 43 method comparisons and 4599 samples met the inclusion criteria. The overall pooled sensitivity was 95.8% (95% CI: 93.6–97.4%), and the specificity was 90.2% (95% CI: 86.8–92.9%). Even with a limited number of comparisons (n = 2), direct culture demonstrated high diagnostic sensitivity (99.1%) and significantly faster turnaround times. Crucially, selective enrichment exhibited a profound species-specific bias: C. jejuni showed 59.4 percent lower recovery than C. coli in Bolton broth, likely due to differential polymyxin B susceptibility. These findings highlight the importance of context-dependent method selection within the ISO 10272-1:2017 framework, suggesting that direct culture (Procedure C) should be prioritized for high-contamination matrices to ensure unbiased recovery of C. jejuni. Large-scale multicenter validation is warranted to confirm these exploratory findings for direct culture. Full article

Background/Objectives: Operational DNA databases traditionally rely on static locus-count thresholds to determine search eligibility and report matches. While computationally straightforward, these rigid criteria routinely discard high-value investigative leads from degraded forensic profiles while simultaneously permitting adventitious matches when common alleles are involved. To overcome the limitations of static rules, this study introduces an automated framework for dynamic likelihood ratio (LR) thresholding. Methods: Utilizing a Fast Fourier Transform (FFT) algorithm, the system calculates the Probability Mass Function (PMF) for any specific combination of shared loci in real-time, natively incorporating the Balding–Nichols model to account for population substructure. Instead of applying an arbitrary locus count or fixed LR cutoff, the framework defines admissibility based on a user-defined maximum upper bound of acceptable false positives at a specified confidence (probability) level (e.g., 95%). Results: This empowers database custodians to precisely predict and adapt their search criteria to match an acceptable administrative workload, dynamically adjusting the required LR threshold to the exact size of the searched database. This approach was validated through massive-scale empirical simulations across five reference population groups. Receiver Operating Characteristic (ROC) and Poisson distribution analyses reveal that static thresholds inevitably collapse under the multiplicity effect of large-scale comparisons; for instance, a static locus rule that maintains safety within a small DNA database yields an unmanageable false positive risk when scaled to larger DNA databases or international networks like the Prüm DNA Exchange. Conclusions: By explicitly coupling the decision threshold to the database size and the genetic rarity of the evidence, this dynamic framework provides a mathematically rigorous and scalable solution. Most notably, it identifies rare, low-locus matches that static rules typically discard, offering a method to maintain a predefined expected false positive rate. Full article

The global construction sector, a major consumer of virgin raw materials, is under increasing pressure to transition from a linear to a circular economy model. Marble waste, generated in large quantities during quarrying, cutting, and polishing operations, represents a promising secondary resource for sustainable construction applications. This systematic review was conducted in accordance with the PRISMA 2020 reporting guidelines to critically evaluate the utilization of marble waste in concrete and other building materials. A comprehensive literature search was performed using major scientific databases, and relevant studies published between 2000 and 2025 were analyzed. The findings consistently indicate that marble waste performs most effectively as a fine aggregate replacement at 10–20%, resulting in improved compressive strength, pore refinement, and durability. As a cement substitute, the optimum replacement level is generally 5–10%, beyond which dilution effects may adversely affect strength development. The performance is primarily attributed to improved particle packing and microstructural refinement. This review further highlights future pathways for industrial-scale implementation, mix optimization, standardisation, and policy integration to accelerate circular construction practices. These findings support the potential of marble waste as a sustainable material in advancing circular economy principles in the construction industry. Full article

Background/Objectives: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are increasingly used for the management of obesity and type 2 diabetes, particularly among women of reproductive age. Emerging evidence suggests potential effects on ovulation, fertility, and pregnancy outcomes. This narrative review aims to synthesize current evidence on the reproductive safety of GLP-1RAs, with a focus on their implications for conception, unintended pregnancy, and maternal–fetal outcomes. Methods: A narrative literature review was conducted using PubMed and relevant bibliographic sources to identify studies published between 2020 and 2025. The search included clinical trials, observational studies, registry data, case reports, and selected preclinical evidence. Studies addressing reproductive outcomes, including ovulation, fertility, pregnancy exposure, and fetal safety, were included. Evidence was synthesized descriptively in accordance with recommended approaches for narrative reviews. Results: Available evidence indicates that GLP-1RAs may improve ovulatory function and menstrual regularity, particularly in women with obesity or polycystic ovary syndrome, potentially increasing the likelihood of conception. However, human data on pregnancy exposure remain limited. While current evidence does not consistently demonstrate a strong teratogenic signal, findings are based on small samples and heterogeneous study designs. Concerns persist regarding unintended pregnancies due to improved fertility and the absence of robust safety data during early gestation. Conclusions: GLP-1RAs present a complex clinical scenario in women of reproductive age, with potential benefits for metabolic and reproductive health but uncertain safety during pregnancy. Clinicians should exercise caution, provide appropriate contraceptive counseling, and carefully weigh the risks and benefits when prescribing these agents. Further large-scale, prospective studies are needed to clarify reproductive safety and inform evidence-based clinical guidelines. Full article

The Flexible Job Shop Scheduling Problem (FJSP) is central to smart manufacturing, yet standard algorithms often prioritize productivity (makespan) at the expense of cost and reliability. This paper introduces t-MOHHO, a collaborative optimization framework designed to equilibrate machine load, processing costs, and delivery timeliness alongside throughput. By incorporating an adaptive Student’s t-distribution mutation operator and a non-linear energy escape mechanism, t-MOHHO effectively navigates high-dimensional search spaces. Extensive validation on 10 MK benchmark instances reveals that t-MOHHO demonstrates significant advantages over classic HHO, MOPSO, and MOEA/D across most metrics. Notably, in comparison to the state-of-the-art NSGA-III, t-MOHHO executes a clear trade-off: it trades marginal makespan efficiency for substantial reductions in cost and tardiness. Specifically, on the large-scale MK10 instance, t-MOHHO reduces total tardiness by 56.2% and lowers processing costs by 3.4% compared to NSGA-III. These results demonstrate that t-MOHHO can strategically sacrifice maximum speed to secure superior punctuality and cost-efficiency, making it a robust decision-support tool for Just-in-Time (JIT) production environments. Full article

Introduction: Pancreatic cancer is one of the most lethal malignancies worldwide, and its incidence continues to rise. Periodontitis, a highly prevalent chronic inflammatory disease, has been linked to several systemic conditions, including a potential increase in pancreatic cancer risk. However, the available epidemiological evidence remains heterogeneous and fragmented. Objective: To evaluate whether periodontitis is associated with an increased risk of pancreatic cancer through a systematic review and meta-analysis of observational studies. Materials and Methods: A comprehensive search was conducted in PubMed, EMBASE, Web of Science, Scopus, the Cochrane Library, ClinicalTrials.gov, and the WHO regional databases, following PRISMA guidelines. Cohort, case–control, and cross-sectional studies assessing periodontitis through clinical parameters, radiographic measures, or tooth loss—and reporting pancreatic cancer risk (HR, RR, or OR)—were included. Risk of bias was assessed using the Newcastle–Ottawa Scale. Random-effects meta-analyses, meta-regressions, leave-one-out sensitivity analyses, influence diagnostics, publication bias assessment, and Trial Sequential Analysis (TSA) were performed. Results: Eight observational studies (primarily cohort designs) (n = 476,245 participants) met the inclusion criteria. Periodontitis was associated with an increased risk of pancreatic cancer (pooled HR = 1.56; 95% CI: 1.28–1.89), with moderate heterogeneity (I² = 55.5%). Sensitivity and influence analyses confirmed the robustness of the estimate. TSA showed a consistent trend, although the cumulative evidence remains insufficient for a definitive conclusion. Conclusions: Observational evidence suggests a modest statistical association between periodontitis and pancreatic cancer risk. However, the absolute risk increase is very small, and Trial Sequential Analysis indicates that cumulative evidence remains insufficient to establish causality or to support preventive or clinical recommendations. Further large-scale prospective studies with standardized periodontal assessments are required. Full article

Fusarium oxysporum f. sp. lycopersici (FOL) is one of the most destructive soil-borne pathogens affecting tomato production worldwide, causing substantial yield losses and persisting in soil for extended periods. The increasing regulatory restrictions on chemical fungicides and the emergence of resistant pathogen strains have intensified the search for sustainable and environmentally friendly alternatives. This systematic review synthesizes studies published between 2000 and 2025 that evaluated the antifungal efficacy of essential oils (EOs), their bioactive constituents, and EO-based nanoformulations against FOL in tomato. A total of 40 studies were included, following the PRISMA 2020 guidelines, encompassing in vitro, greenhouse, and limited field evaluations. Many EOs rich in phenolic compounds and oxygenated monoterpenes, such as thymol, carvacrol, eugenol, citral, and menthol, consistently inhibited FOL growth and spore germination, with reported mycelial growth inhibition ranging from 60 to 100% and minimum inhibitory concentrations (MICs) between 0.05 and 1.5 µL ml⁻¹. However, the use of EOs is often limited because they evaporate quickly, do not mix well with water, can harm plants, and do not persist under field conditions. Nano-delivery systems, including nanoemulsions, polymeric nanoparticles, chitosan-based carriers, and lipid-based nanostructures, have been shown to enhance the stability, bioavailability, and antifungal efficacy of EOs. This has led to improved disease management and reduced pesticide application rates. In addition, several EO-based treatments have been reported to activate plant defense responses, including the induction of defense-related genes, antioxidant enzymes, and epigenetic modifications. Overall, EO-based nanoformulations show promise as next-generation biopesticides for the sustainable management of tomato Fusarium wilt. Nevertheless, large-scale field validation, standardized formulation protocols, and regulatory assessments are required before these technologies can be widely implemented in agriculture. Full article