Search Results (3,423)

Background: Since 2014, all Australian jurisdictions have progressively amended legislation to authorise pharmacists to administer vaccines, evolving from restricted pilots to an essential public health pillar. Objective: This review analyses the longitudinal evolution of pharmacist-administered vaccinations (PAVs), documenting changes in authorised vaccines, age eligibility, and regulatory frameworks across all Australian jurisdictions. Methods: A retrospective review of Australian jurisdictional legislation, regulations, and policy documents was undertaken. Searches included official legislative registers, Government Gazettes, Health Department protocols, and professional guidance published by Pharmaceutical Society of Australia (PSA) and The Pharmacy Guild of Australia between 2014 to 2026. Documents were independently reviewed by five authors, followed by secondary verification and consensus-based adjudication to resolve discrepancies and confirm findings. Results: PAVs scope was expanded from a single influenza pilot in 2014 to include over 21 vaccine-preventable diseases by 2026. The COVID-19 pandemic catalysed rapid reform, leading to the standardisation of age eligibility (largely ≥5 years). A landmark milestone occurred in 2025 when South Australia enabled pharmacists to administer any vaccine within their professional scope. Conclusion: Legislative reforms have significantly enhanced vaccine accessibility. However, jurisdictional fragmentation persists. National harmonisation, using a competency-based model similar to South Australia, is recommended to streamline delivery and optimise public health outcomes. Full article

Background: Breast cancer remains a leading cause of cancer-related mortality, and reliable computational decision support is increasingly viewed as a complement to expert pathological assessment rather than a replacement for it. Variational quantum classifiers (VQCs) and Quantum Support Vector Machines (QSVMs) have recently been promoted as candidate models for medical classification, yet most published comparisons rely on internal hold-out validation alone and report only a single point estimate of discrimination, omitting calibration, decision-analytic value, and explainability—three ingredients that any clinically credible model must furnish. Methods: We assembled a complete quantum–classical machine learning pipeline and evaluated it under a deliberately stringent protocol designed to expose, rather than conceal, the limitations of current Noisy Intermediate-Scale Quantum (NISQ)-era models. The analytical hypothesis was conservative and stated in advance; in light of saturated classical baselines on this benchmark, we did not anticipate a quantum advantage in raw discrimination, and we framed the study as a methodological probe rather than as a competition. Using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset (n = 569) for development and an independent Wisconsin Original (WBC) cohort (n = 683) for external validation, we benchmarked five classical learners (XGBoost, LightGBM, CatBoost, RandomForest, RBF-SVM), two quantum models (an eight-qubit VQC implemented in PennyLane and a ZZ-feature-map QSVM implemented in Qiskit), and a stacked hybrid ensemble. The evaluation framework combined Optuna-driven hyperparameter optimisation, internal–external cross-validation, and external validation on the independent WBC cohort. Robustness and interpretability were then probed through circuit depth and embedding rotation ablation, depolarising noise stress tests, learning curve and feature stability analysis, decision curve analysis, and dual SHAP-based explanations covering both a direct tree-based explanation and a quantum surrogate. Reporting followed the TRIPOD + AI guideline. Results: On the internal test partition, RBF-SVM achieved the highest discrimination (AUC = 0.998), with XGBoost, LightGBM, CatBoost, the hybrid ensemble, and the VQC clustering between 0.992 and 0.996; the QSVM with a ZZ-fidelity kernel underperformed substantially (AUC = 0.727). Pairwise tests for correlated ROC curves indicated that most differences among top models were not statistically significant. On the external WBC cohort, model rankings reorganised, as RBF-SVM (AUC = 0.986, 95% CI 0.946–0.997), RandomForest (0.985, 95% CI 0.945–0.996), VQC (0.983, 95% CI 0.942–0.995), and the hybrid ensemble (0.982, 95% CI 0.941–0.995) all retained near-ceiling discrimination with extensively overlapping confidence intervals. Ablation analysis demonstrated that the choice of embedding rotation is decisive—Z-rotation embeddings collapsed VQC performance to chance levels (AUC ≈ 0.50), whereas X- and Y-rotations preserved it. Depolarising noise up to p = 0.10 had a negligible effect on the VQC, and SHAP analyses converged on worst concave points, mean concave points, and worst area as the dominant predictors across both classical and quantum models. Decision curve analysis showed positive net benefit for both classical and hybrid models across the clinically meaningful threshold range, exceeding both the treat-all and treat-none reference strategies throughout. Conclusions: In the present regime, the principal contribution of QML is not raw discrimination—modern classical learners are already at the data ceiling—but the construction of a rigorous, reproducible, externally validated, and interpretable benchmarking framework in which quantum models can be fairly compared with their classical counterparts. Because evaluation was confined to curated benchmark datasets rather than real-world clinical populations, the interpretability and net benefit findings reported here should be read as benchmark-level evidence and not as a demonstration of readiness for clinical deployment. Full article

Wireless-power mobile edge computing, SWIPT-MEC, priority-aware WPT scheduling and swarm resource allocation already solve important parts of the energy-management problem. The novelty of WPT-JCCO is not any one of those elements; it is a single swarm-supervisory feasible set that couples decisions which the three adjacent method classes normally separate. Each epoch-level action jointly selects the robot to charge and one of three physically distinct WPT modalities: far-field radio-frequency, resonant near-field and directional lightwave transfer, together with the SWIPT split, local/edge task placement, CPU frequency, bandwidth and transmit power. Relative to SWIPT-MEC, the formulation adds discrete recipient–modality selection with pose, alignment, blockage and dwell-dependent feasibility. Relative to conventional WPT scheduling, charging is not a separate priority or routing stage but is solved jointly with computation and radio allocation. Relative to swarm resource-allocation methods, energy replenishment is endogenous and an individual minimum-battery constraint protects the weakest robot. A fourth coupling makes the centrally generated resource vector admissible only when the complete sense–compute–actuate age fits the one-second supervisory epoch; otherwise a previously feasible or local-safe action is applied. Nonlinear harvesting, partial offloading, priority scoring and augmented-Lagrangian primal–dual updates are treated as established techniques. This paper derives the continuous block updates, keeps the WPT variables binary through candidate screening, and declares convergence only when stationarity, feasibility, merit-change and binary-hold tests are jointly satisfied. Normalised primal steps are safeguarded by backtracking, dual and penalty updates are bounded, and a local tracking bound plus divergence monitor delimit real-time operation without claiming global mixed-integer optimality or closed-loop motion stability. Numerical evaluation over a 20-robot swarm and 30 Monte Carlo runs shows that WPT-JCCO reduces net energy depletion by 23.8% relative to communication–computation optimisation with static WPT and by 49.7% relative to local-only execution, while increasing task success from 93.5% to 97.3%. A released common-trace comparison shows normalised-cost reductions of 11.1%, 11.3% and 5.8% relative to two-stage WPT+CCO, fixed-SWIPT dynamic offloading and an offline Q-learning scheduler. Convergence and one-factor-at-a-time sensitivity studies further examine swarm size, task load, WPT budget, bandwidth, edge capacity, mobility and channel margin. The headline values remain scoped to the nominal independent-task case; mode-specific RF, near-field and lightwave operating envelopes, robust pose/CSI, WPT-safety and task-DAG extensions are formulated but not presented as hardware-validated results. Full article

Background: β-haemoglobinopathies, including sickle cell disease and transfusion-dependent β-thalassaemia, are among the most common monogenic disorders worldwide and represent a major global health burden. Conventional treatments, such as blood transfusions, iron chelation, fetal haemoglobin induction, and allogeneic haematopoietic stem cell transplantation, have improved outcomes but remain limited by treatment-related toxicity, donor availability, and incomplete curative potential. Methods: A narrative literature review was conducted using PubMed up to 2025. Search terms included “sickle cell disease,” “sickle cell anemia,” “β-thalassemia,” “transfusion-dependent beta-thalassemia,” “gene therapy,” “gene addition,” “gene editing,” “CRISPR-Cas9,” “lentiviral vector,” “children,” “paediatric,” and “pediatric.” Relevant clinical trials, reviews, consensus statements, and guidelines were selected and qualitatively analysed. Results: Gene therapy for β-haemoglobinopathies is based mainly on two strategies: gene addition and gene editing. Gene addition uses lentiviral vectors to introduce functional or modified β-globin genes into autologous haematopoietic stem cells, whereas gene editing targets regulatory pathways, particularly BCL11A, to reactivate fetal haemoglobin synthesis or correct disease-causing mutations. Clinical studies have shown encouraging outcomes, including transfusion independence in many patients with β-thalassaemia and marked reduction or elimination of vaso-occlusive crises in sickle cell disease. Paediatric and adolescent data are increasingly promising, although still limited. Conclusions: Gene therapy is reshaping the treatment landscape of β-haemoglobinopathies by offering a personalised and potentially curative approach. However, long-term safety, conditioning toxicity, fertility preservation, accessibility, costs, and implementation in high-prevalence regions remain critical challenges. Further studies are needed to optimise patient selection and expand equitable access. Full article

Background/Objectives: Carotid body tumour (CBT) resection carries substantial haemorrhage and cranial neuropathy risks. While preoperative embolisation mitigates these, its impact on patient-reported outcomes (PROMs) and quality of life (QoL) remains underexplored. Evaluate the preoperative embolisation’s impact on postoperative QoL using the 36-Item Short Form Health Survey (SF-36) questionnaire. Methods: A retrospective cohort study (68 patients) compared Preoperative Embolisation (Group E, n = 24) and Primary Resection (Group NE, n = 44), adjusting for confounders via multivariate linear regression. Results: Group E featured larger, more complex tumours. Despite this structural burden, intraoperative bleeding was significantly lower in Group E (median 300, Interquartile Range (IQR) 150–400 vs. 400 mL, IQR 350–500; p = 0.012). Group E reported lower overall median SF-36 scores (59.5 vs. 70 points; p = 0.002); however, multivariate analysis confirmed that embolisation was not an independent negative QoL predictor (b = −0.52, p = 0.852), whereas Shamblin grade III was associated with diminished well-being (b = −7.42, p = 0.012). Domain analysis revealed selective restrictions driven by acute somatic and emotional stress: Physical Functioning (p = 0.002), Bodily Pain (p = 0.007), General Health (p = 0.003), Vitality (p = 0.016), and Role Emotional (p = 0.010). Age stratification revealed a non-linear trend, validated via ANOVA (p = 0.013): working-age patients (<60 years) exhibited significantly lower SF-36 scores (61.2 ± 11.4 points) than the intermediate (p = 0.034) and elderly (p = 0.011) subgroups (>70 years; 72.8 ± 5.1 points). Conclusions: Preoperative embolisation optimises hemodynamic control and surgical safety without independently compromising long-term well-being. Postoperative QoL is heavily modulated by age-dependent generational psychosocial baselines rather than structural morbidity metrics alone. Full article

Sorghum is an important multi-purpose crop used for food, feed, brewing, and bioenergy. However, mechanised harvesting is hindered by its tall stature, complex panicle morphology, small and fragile grains, high-moisture stems and leaves, and susceptibility to lodging at maturity. This review provides a PRISMA-guided systematic literature search and narrative synthesis of mechanised sorghum harvesting from crop adaptability to intelligent equipment. The main literature search covered publications from January 1990 to May 2026 and included Web of Science Core Collection, Scopus, ScienceDirect, SpringerLink, Google Scholar, and other relevant sources. A total of 1928 records were identified, and 190 studies were finally included in the qualitative synthesis after duplicate removal, title-and-abstract screening, and full-text assessment. The review analyses how plant morphology, panicle exsertion, physical and mechanical properties, maturity stage, moisture content, varietal differences, and lodging affect harvesting suitability. The applicable conditions for segmented harvesting, panicle harvesting, direct grain harvesting, and multi-purpose coordinated harvesting are compared under different crop, regional, and machinery conditions. Key technological advances are synthesised in relation to header feeding, threshing and cleaning, straw management, and operating-parameter optimisation. Recent developments in machine vision, condition monitoring, adaptive control, DEM and CFD-DEM simulation, and digital twins are also assessed. The analysis shows that high-quality mechanised sorghum harvesting requires the coordinated optimisation of crop traits, harvesting methods, machine structures, operating parameters, and digital sensing–control feedback. The main contribution of this review is to establish an integrated crop–machine–operation framework for identifying technical constraints, comparing harvesting routes, and guiding the development of specialised, low-loss, low-breakage, and intelligent sorghum harvesting systems. Full article

Titanium alloys are among the most important biomaterials due to their good biocompatibility, high corrosion resistance and favourable mechanical properties. Particular interest is directed towards β-Ti alloys, whose properties can be tailored by adding β-stabilisers such as molybdenum and chromium, with the aim of developing materials suitable for biomedical applications. This paper investigates the influence of chemical composition on the phase composition, microstructure, microhardness and corrosion properties of experimental Ti-Mo-Cr alloys produced by casting. Phase composition was determined by X-ray diffraction analysis (XRD), while microstructural characteristics were analysed by scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS). The results showed that increasing the molybdenum and chromium content contributes to the stabilisation of the β-phase and reduces the proportion of α and α″ martensite. Complete stabilisation of the β-phase was achieved in the Ti-10Mo-30Cr alloy, while the Ti-10Mo-10Cr alloy showed a dominant presence of α″ martensite. EDS analysis confirmed the segregation of alloying elements during solidification. Microhardness measurements showed an increase in hardness with increasing total alloying element content, with the highest hardness measured in the Ti-20Mo-20Cr alloy. Corrosion properties were tested using open circuit potential (OCP), electrochemical impedance spectroscopy (EIS) and Tafel polarisation methods in 0.9% NaCl (sodium chloride) medium. Among the alloys investigated, Ti-20Mo-20Cr showed a favourable overall balance of electrochemical properties, while Ti-10Mo-30Cr exhibited the lowest corrosion rate. The results suggest that a balanced ratio of molybdenum and chromium plays a key role in optimising the microstructure, mechanical properties, and corrosion performance of Ti-Mo-Cr alloys. Full article

The increasing demand for lithium-ion batteries (LIBs) raises concerns about the security of critical raw material supply and the management of hazardous waste. Efficient recycling can alleviate these issues by transforming spent batteries into high-value secondary materials for the circular economy. Industrial recycling has traditionally focused on the recovery of nickel (Ni) and cobalt (Co), whereas lithium (Li) recovery has often been sidelined due to technical complexities and fluctuating economic incentives. To meet the European Union (EU) Batteries Regulation target of 80% lithium recovery by the end of 2031, technically effective and economically viable lithium recovery strategies are required. This study investigates the use of food-grade sucrose as an organic reductant for the targeted recovery of lithium from NMC622 and NCA battery materials. The process combines sucrose-assisted reductive roasting with selective water leaching. The effects of roasting temperature, holding time, sucrose dosage, and heating rate were systematically evaluated and optimised. Under the best conditions of 600 °C, 15 min, 15 wt% sucrose, and a heating rate of 20 °C/min, lithium leaching efficiencies of 93.2% and 87.6% were achieved for separated NMC622 cathode material and NMC622-derived black mass, respectively. The method was also applicable to NCA-based black mass, reaching 83.7% lithium recovery under the same conditions. Mechanistic analysis revealed that lithium release was strongly controlled by the extent of transition metal reduction. Cobalt was fully reduced to its metallic state under all tested conditions. However, maximum lithium recovery required nickel to be reduced to metallic Ni and manganese-containing phases to be converted to MnO. The sucrose-assisted roasting process was rapid and holding times longer than 15 min decreased lithium recovery. This decrease was caused by the formation of poorly soluble lithium-containing phases, such as LiF and Li₃PO₄. F composition analysis showed the black mass (1.06 wt%) and anode fractions (2.26 wt%) to contain significantly more F than the cathode fraction (0.46 wt%), hence leading to the 5% Li leaching efficiency difference between cathode and black mass fractions under most conditions tested. Overall, these results demonstrate that sucrose-assisted reductive roasting, followed by selective water leaching, provides a rapid and effective route for high-efficiency lithium recovery from NMC- and NCA-based battery materials. Full article

This paper examines building performance under extreme conditions by treating thermal resilience as a process-based and time-dependent property. Existing approaches remain fragmented: resentation, extreme event definition, resilience metrics, passive strategies, and energy systems are typically handled in isolation, leaving current methods with limited capacity to explain how buildings respond to prolonged disruptions such as heatwaves or power outages. The study offers a cross-domain synthesis of thermal resilience research, drawing together climate modelling, indoor thermal response, resilience metrics, passive design strategies, distributed energy systems, and regulatory constraints. Building on this synthesis, a trajectory-based framework is developed that links climate inputs, event definition, indoor thermal response, and performance metrics within a unified structure. This integration is extended into architectural design through a decision-oriented framework that interprets resilience as the outcome of a hierarchy of decisions structured by reversibility and operational dependence. Early design decisions define the constraints and the range of achievable performance within which subsequent optimisation occurs. Building performance emerges from the interaction of passive strategies and energy-supported systems under constrained conditions. The results establish that thermal resilience cannot be inferred from conventional indicators; it must be understood through the temporal evolution of indoor conditions. The proposed framework provides a consistent basis for linking resilience assessment with design decision-making, supporting a unified approach to resilience-oriented design. Full article

Robust perception in adverse weather conditions remains a significant challenge for autonomous vehicles. Short-wave infrared (SWIR) sensors offer specific physical properties that enable them to penetrate atmospheric disturbances like fog, rain, and snow. However, effectively combining this robustness with the textural and colour information of visible (VIS) cameras is difficult due to signal decorrelation and the limitations of static fusion schemes. To address this, we present VISWIR (Visible and SWIR Weighted Image Reconstruction), a pixel-level fusion method based on a multi-scale pyramid architecture. We introduce an automated strategy for scheduling parameters based on weather conditions using an optimisation framework. Rather than relying on static weights, our method applies offline parameter scheduling to adjust fusion hyperparameters based on the meteorological context. We focus on a multi-objective optimisation approach that maximises perceptual image quality via No-Reference Image Quality Assessment (NR-IQA) metrics. Validated in controlled environment scenarios with varying weather severities, our results confirm the potential of VISWIR as a robust, lightweight algorithmic baseline to enhance the perception capabilities of autonomous vehicles in adverse weather conditions. Full article

The financial early warning mechanism of listed companies has an important strategic value for maintaining the stability of the capital market and preventing systemic financial risks. This study proposes a hybrid model (EBWO-BP) based on the improved beluga optimisation algorithm (EBWO) and BP neural network for financial early warning research. Innovative T-SNE nonlinear dimensionality reduction technique is applied to the multidimensional evaluation system constructed by 23 financial and two non-financial indicators. The empirical evidence based on the data of A-share listed companies in 2022–2024 shows that the accuracy of the EBWO-BP test set reaches 86.51% (AUC = 0.83), which demonstrates a significant prediction advantage compared with the optimisation algorithm models such as GA-BP and PSO-BP, as well as the CNN and LSTM deep learning models; when the sample size is increased to 700 groups, the accuracy is improved to 89.05%, verifying the model robustness. The method achieves significant improvement of financial risk prediction through algorithm fusion innovation, and provides methodological innovation and practical reference for intelligent financial risk monitoring. Full article

Emerging-market equity exchanges require regime forecasting systems that are continuous in time, robust to heavy-tailed distributions, and optimised against false alarms. No existing method addresses all three simultaneously, and no prior study has reported a crisis false-alarm rate on JSE equities. We propose S-NODE-ANF-RRC: a stochastic neural ODE within an Adaptive Neuro-Fuzzy Risk-Regime Clustering architecture, integrated by a Milstein scheme with Lyapunov-regularised dual-loss training. The system is evaluated as a one-step-ahead probabilistic forecaster ($h=1$ trading day) on 2696 daily observations across 17 JSE securities (March 2015–March 2026). Gaussian mixture clustering on raw features (kurtosis 54.8) inflates ARI by 1.3×; log-transformation corrects this artefact. Two operational profiles emerge: the N-ODE-ANF-RRC achieves the lowest cost (10,350 bp, 65.1% below GMM) and longest lead time (0.71 days); the S-NODE-ANF-RRC achieves the lowest false alarm rate among probabilistic architectures (FAR = 0.051), with a 42.0% cost reduction versus GMM (McNemar $p=0.027$, power $1-\beta =0.73$; bootstrap CI [5250, 19,600] bp excludes zero). Ablation confirms drift, diffusion, and dual-loss as the minimum viable daily-frequency configuration. Full article

Background/Objectives: Sapropterin dihydrochloride is an established treatment option for individuals with phenylketonuria (PKU) who demonstrate responsiveness, but uncertainty persists regarding dosing frequency, timing relative to meals, the influence of dietary composition, and efficacy of different formulations. Despite widespread use in the UK, real-world administration behaviours have not previously been characterised. This study aimed to characterise sapropterin administration behaviours among people with PKU in the UK. Methods: A 31-item questionnaire was developed and disseminated via the National Society for Phenylketonuria website and social media channels. The survey captured demographic information, dosing schedules, formulation use, administration techniques, co-ingestion with food, and changes in natural protein tolerance following initiation of generic sapropterin. Results: 124 current sapropterin users completed the survey. Most respondents were caregivers of children or adolescents (68.5% aged 0–18 years). Once-daily dosing was most common (66.1%, n = 82), typically administered at breakfast, followed by twice-daily (32.3%, n = 40) and three-times-daily (1.6%, n = 2). Tablets were the predominant formulation (92.7%, n = 115); 50.4% (n = 58/115) swallowed tablets whole, while the remaining (49.6%, n = 57/115) crushed or dissolved them in water or juice. Nine respondents (7.3%, n = 9/124) used powder sachets. Most participants (75%, n = 93/124) took sapropterin with food, with both low-fat (36.6%, n = 34/93) and high-fat (26.9%, n = 24/93) meals reported. Over a third of participants (33.9%, n = 42/124) tolerated a natural protein intake >30 g/day when this was measured, and a further 15.3% (n = 19) were able to maintain a fully unrestricted protein intake without protein substitute supplementation. The magnitude of protein intake improvement was significantly greater among adults (p < 0.001), those with higher baseline natural protein intake (≥30 exchanges/day) (p < 0.001), and individuals who swallowed sapropterin tablets whole (p = 0.038). Although 71.8% (n = 89/124) were pleased with their increased natural protein allowance, many expressed a desire for further improvement. Conclusions: Substantial heterogeneity in dosing schedules, formulation handling, and co-ingestion practices highlights the absence of standardised guidance. These findings emphasise the need for clearer clinical recommendations to optimise treatment effectiveness and support consistent, equitable care. Full article

Background/Objectives: Peri-implant diseases, encompassing peri-implant mucositis and peri-implantitis, affect 43% and 18.8–23% of implant-bearing patients, respectively, representing significant clinical challenges in implant dentistry. While mechanical debridement remains foundational, biologically active materials offer promising adjunctive regenerative strategies. This narrative review synthesises current evidence regarding five biologically active materials: enamel matrix derivative (EMD), platelet-rich fibrin (PRF), fibroblast growth factor-2 (FGF-2), recombinant human platelet-derived growth factor-BB (rhPDGF-BB/GEM 21S^®), and polynucleotide–hyaluronic acid combinations (Regenfast^®). Methods: The relevant literature was identified using electronic databases, including MEDLINE, PubMed, Scopus, and Google Scholar. This review focused on clinical studies and randomised controlled trials with a minimum follow-up of six months investigating biologically active materials in peri-implant disease management. Material mechanisms, clinical efficacy, therapeutic limitations, and evidence quality were systematically evaluated. Attention was directed toward identifying genuine biological distinctions between peri-implant and periodontal disease contexts. Results: EMD demonstrates efficacy exclusively within multimodal surgical protocols, with isolated application yielding limited benefits. rhPDGF-BB shows superior periodontal regenerative capacity; however, dedicated peri-implantitis trials remain absent. FGF-2 exhibits paradoxical osteogenic suppression despite bone fill achievement, limiting peri-implant applicability. PRF and Regenfast^® demonstrate a mechanistically sound rationale yet lack substantive peri-implant disease validation. The critical findings revealed that peri-implant regeneration fundamentally differs from periodontal regeneration: implants lack periodontal ligament anatomy, rendering ligamentogenic differentiation-promoting agents biologically inappropriate. Conclusions: Contemporary biologically active materials demonstrate compelling periodontal efficacy yet remain inadequately validated for peri-implantitis management. This disparity reflects authentic biological distinctions rather than insufficient investigation. Until multicentre randomised controlled trials stratify efficacy across distinct peri-implant disease presentations, practitioners must prioritise evidence-based surgical fundamentals—meticulous decontamination, strategic grafting, and optimised wound healing—integrating biologically active materials judiciously within comprehensive, anatomy-respecting treatment protocols. Full article

Background: Circulating cell-free DNA (cfDNA) enables minimally invasive tumour genomic profiling, yet simultaneous interrogation of mutations and DNA methylation remains limited by assay complexity and input constraints. Methods: Here, we evaluate the Agilent Avida Duo system, a single workflow integrating high-sensitivity mutation detection with targeted DNA methylation analysis. We analysed 21 stage III and IV melanoma patient samples. Results: The Avida Duo mutation assay detected mutant allele frequencies (MAFs) down to 0.05% and identified tumour-associated mutations in all melanoma patients, including within GC-rich regions such as the TERT promoter. Optimisation of the Avida Duo mutation workflow, using TapeStation-quantified cfDNA and reduced amplification cycles, improved library consistency without compromising sensitivity. Methylation profiling of the melanoma baseline cohort with the Avida Duo methylation panel showed high concordance with QIAseq targeted methylation results, with the mean cfDNA fraction methylation ranging from 0.051–0.079 in most patients and reaching 0.249 in the patient with the highest ctDNA burden (MAFs up to 35.4%). Conclusions: Our findings demonstrate that the Avida Duo workflow enables simultaneous, high-resolution detection of mutation and methylation profiles from a single cfDNA sample, streamlining processing and enhancing molecular insight for clinical and translational applications. Full article