Search Results (933)

Pediatric drug therapy remains fundamentally challenged by profound interindividual variability driven by dynamic development, genetic, and environmental factors. Although dosing strategies based on age, body weight, or body surface area remain important starting points in pediatric pharmacotherapy, they may not fully capture ontogeny-dependent variability in drug disposition and response. Consequently, clinically relevant differences in efficacy and toxicity may still occur among children receiving similar weight-adjusted doses. Pharmacogenomics offers a promising framework for individualized therapy; however, its clinical translation in pediatrics is limited by developmental variability in gene expression and enzyme activity. Emerging evidence highlights the pivotal role of epigenetic regulation, including DNA methylation, histone modifications, and microRNAs, in modulating pharmacogenetic expression across developmental stages, thereby reshaping drug response trajectories. Concurrently, advances in artificial intelligence and next-generation sequencing enable integration of multidimensional datasets, facilitating predictive modeling of drug efficacy and toxicity. This narrative review provides a comprehensive synthesis of developmental pharmacology, pharmacogenomics, and epigenetic mechanisms, while critically evaluating current translational gaps and implementation challenges. Importantly, it proposes an integrative precision framework that incorporates genetic, epigenetic, and computational insights to optimize pediatric pharmacotherapy. By bridging mechanistic biology with emerging digital health technologies, this work advances a paradigm shift from empirical prescribing toward predictive, adaptive, and individualized therapeutic strategies. The proposed approach holds significant potential to enhance clinical outcomes, minimize adverse effects, and accelerate the realization of precision medicine in pediatric populations. Full article

Appropriate dose adjustment of renally eliminated medicines is central to safe pharmacotherapy in patients with chronic kidney disease; yet, in European primary care, it is systematically undermined not by lack of knowledge, but by structural misalignment between laboratory reporting, regulatory product information, and clinical guidelines. This Perspective argues that the core barrier to optimal renal dose adjustment is a mismatch between routinely reported indexed eGFR and dosing requirements based on absolute renal function, compounded by persistent regulatory reliance on the Cockcroft–Gault equation despite its known limitations. We show how these structural inconsistencies, together with patient-related factors such as frailty, ageing, and body size, generate uncertainty at the point of prescribing and contribute to persistent variability in dosing decisions. To address this challenge, we propose a structured, context-aware renal dosing framework designed for routine primary care. The framework integrates regulatory guidance, multiple methods of renal function estimation, and patient-specific modifiers into a stepwise decision process. Clinical vignettes illustrate how divergent renal function estimates and regulatory requirements can lead to different dosing decisions in everyday practice. By reframing renal dose adjustment as a context-driven clinical process rather than a purely equation-based task, this Perspective highlights the need for regulatory alignment and pragmatic decision tools to improve prescribing quality in patients with chronic kidney disease. Full article

Cone-beam computed tomography (CBCT) is widely used in dentomaxillofacial imaging, but its expanding use requires cautious appraisal of stochastic risk and dose optimization. This updated structured narrative review summarizes evidence on organ dose, effective dose, modeled cancer-risk estimation, cumulative exposure, diagnostic reference levels, and patient shielding in dental CBCT. PubMed/MEDLINE and Scopus searches were updated to 15 May 2026. Overall, 24 primary studies were synthesized: 9 addressing dose, diagnostic reference levels, cumulative exposure, or cancer-risk modeling, and 15 evaluating shielding or radiation-protection strategies. Dose and modeled risk varied markedly according to scanner type, field of view, exposure parameters, anatomical region, age, sex, and repeat imaging. The salivary glands, oral mucosa, thyroid, and eye lens were the most relevant exposed organs; children, female patients, and patients undergoing repeated imaging represented the most vulnerable groups. Shielding studies reported substantial dose reductions in selected protocols, but the benefit depended on shield design, positioning, field of view, and image-quality impact. Dental CBCT should be prescribed only when three-dimensional information is expected to change management and should be optimized through the smallest adequate field of view, low-dose protocols, cumulative-dose awareness, and selective shielding when diagnostically appropriate. Full article

This study evaluated the association between vitamin D3 levels, transplanted kidney function, and body composition in 315 stable renal transplant recipients (median 7.7 years post-transplant). The biochemical profile included eGFR, PTH, calcium, phosphorus, and 25(OH)D₃ levels. Vitamin D status was defined as deficiency (<20 ng/mL), insufficiency (20–30 ng/mL), or optimal (>30 ng/mL). Body composition was assessed via bioelectrical impedance analysis, capturing parameters such as BMI, visceral fat area, and phase angle. Multivariable quantile regression models were used to assess the associations between clinical/metabolic parameters and graft function. Vitamin D3 supplementation was prescribed in 61.5% of patients, with 49.7% receiving active analogues and 50.3% cholecalciferol. Results showed that 25(OH)D₃ levels did not correlate with graft function in the total population, and no significant differences in eGFR were observed regarding vitamin D status. In multivariable models, 25(OH)D₃ levels correlated significantly only with calcium levels. No significant correlations were observed between vitamin D and transplant vintage, age, eGFR, or any anthropometric and body composition parameters. Full article

Degree-based topological indices play a central role in characterizing graph structures and their chemical applications. Among these, multiplicative Zagreb indices have attracted considerable attention due to their strong discriminative power and relevance in chemical graph theory. Neighborhood versions of these indices extend the classical concept by incorporating the aggregate degree information of adjacent vertices, capturing more subtle structural effects related to local branching. Trees, as connected acyclic graphs, provide a natural and tractable class for studying the extremal behaviors of these indices, while molecular trees—trees with a maximum degree of at most four—serve as chemically meaningful models of acyclic organic compounds. Investigating extremal values on these structures offers both theoretical insight into the indices’ behavior and identification of molecular graphs that maximize or minimize them. In this work, we determine the maximal and minimal values of the neighborhood-based multiplicative Zagreb indices for trees of fixed order and prescribed maximum degree, and we provide a complete structural characterization of all extremal graphs. Special attention is given to molecular trees, for which explicit extremal bounds are derived and all optimal structures are identified. These results provide efficient tools for evaluating the indices and illuminate the structural principles governing their extremal behavior. Full article

Pediatric respiratory infections remain among the leading causes of emergency department visits, hospitalization and pediatric intensive care unit (PICU) admission. Although most acute respiratory infections in children are viral, clinical manifestations overlap substantially among viral, bacterial and atypical pathogens, creating diagnostic uncertainty and promoting empirical antimicrobial use. Rapid antigen tests, nucleic acid amplification tests, multiplex respiratory panels and metagenomic sequencing have expanded the ability to detect pathogens within clinically actionable timeframes. However, evidence from pediatric emergency trials indicates that rapid pathogen detection alone does not necessarily reduce antibiotic prescribing or healthcare costs. These findings suggest that the value of rapid diagnostics depends less on analytical breadth than on whether testing is applied to the right child, in the right clinical scenario and within a predefined decision pathway. This narrative review reorganizes the evidence around a scenario-driven top-pathogen framework. Top pathogens are defined as organisms that, in a specific age group, syndrome, season or care setting, have high prevalence, severe disease potential, transmissibility, treatment implications, antimicrobial resistance relevance or infection-control value. We discuss how top-pathogen testing should differ across emergency triage, inpatient ward management, severe pneumonia, PICU care, hospital-acquired pneumonia, ventilator-associated pneumonia and outbreak settings. We further examine the economic mechanisms through which rapid testing may generate value, including reduced unnecessary antibiotics, timely antiviral therapy, optimized isolation, shorter length of stay, reduced repeated testing and prevention of healthcare-associated transmission. Finally, we propose implementation principles centered on diagnostic stewardship, antimicrobial stewardship, local epidemiology and real-world cost-effectiveness evaluation. A scenario-driven top-pathogen strategy may provide a practical bridge between broad syndromic testing and precision infectious disease management in children. Full article

Deep excavation dewatering is an energy-intensive groundwater control process in underground engineering, especially under strong recharge and heterogeneous hydrogeological conditions. Conventional dewatering designs often rely on conservative pumping schemes to ensure the required drawdown, which may generate redundant groundwater extraction, unnecessary electricity consumption, additional carbon emissions, and excessive drawdown-induced settlement. To address this problem, this study develops a coupled improved genetic algorithm and MODFLOW optimization model, termed IGA-M, for dewatering well-group operation under engineering safety constraints. The purpose of the proposed model is not to reduce pumping arbitrarily, but to identify and eliminate redundant pumping while satisfying prescribed requirements for target water levels, settlement control, and hydraulic-gradient safety. Through the FloPy interface, the Improved Genetic Algorithm is dynamically linked with MODFLOW to establish a closed-loop simulation-optimization framework. In each optimization iteration, candidate well operation schemes are automatically transferred to MODFLOW, and the simulated hydraulic heads and settlement responses are returned to evaluate the objective function and safety constraints. In this framework, groundwater extraction, electricity consumption, carbon emissions, and land subsidence are treated as physically linked performance indicators of the optimized dewatering scheme. Validation using an idealized case shows that, under the same safety requirements, the IGA-M model reduces redundant hydraulic loading compared with the traditional uniformly distributed pumping method. By removing redundant pumping beyond the safety requirement, the optimized scheme reduced groundwater extraction by 62.7%, which was accompanied by a 44.9% decrease in both carbon emissions and comprehensive costs, as well as a 57.7% reduction in settlement at observation points. In a practical high-permeability deep excavation adjacent to the Yellow River, the model achieved well-group flow regulation under strong recharge conditions. Compared with the traditional scheme, it eliminated approximately 661,000 m³ of redundant groundwater extraction, corresponding to a 17.7% decrease, and consequently saved 26,800 kWh of electricity and reduced CO₂ emissions by nearly 16,000 kg during the dewatering period. These results demonstrate that the proposed IGA-M framework can transform MODFLOW from a post-design verification tool into an active optimization engine for dewatering design. It provides a physically based decision-support method for reducing redundant pumping and improving energy efficiency while maintaining engineering safety. Full article

Many deterministic multi-objective optimization methods generate Pareto outcomes by repeatedly solving scalarized subproblems for different preference or reference vectors. When the number of objectives is $m\ge 3$, the resulting samples lie on an $(m-1)$-dimensional Pareto surface in objective space. For tasks such as visualization, trade-off exploration, interactive decision making, and sensitivity analysis, a finite cloud of non-dominated points may be insufficient; one often needs a continuous surrogate of the Pareto surface together with a quantitative control of its reconstruction error. This paper studies the corresponding outer-loop reconstruction problem: how should new reference vectors be selected so as to reconstruct the Pareto surface to a prescribed uniform accuracy while using as few scalarized solves as possible? We propose Certified Adaptive Triangulation Sampling (CATS), a curvature-aware adaptive triangulation method for reconstructing a Pareto surface from an oracle $u\mapsto z\left(u\right)$, $u\in {\Delta }_d$, where $d=m-1$. CATS builds a simplicial mesh over the reference simplex and refines the cell with the largest local interpolation quantity $\eta \left(\tau \right)=\frac{1}{2}\left({max}_k{M}_{\tau ,k}\right)\mathrm{diam}{\left(\tau \right)}^2$, where $M_{\tau ,k}$ is an upper bound on the Hessian norm of the kth component of the oracle-induced map over $\tau$. This quantity matches the natural error scale of affine interpolation for $C^2$ maps. The rigorous certified interpretation of CATS applies when the preference-to-Pareto map is single-valued, $C^2$, and equipped with reliable local Hessian-norm upper bounds. If such bounds are replaced by numerical curvature estimates, the same rule can still be used as an adaptive refinement indicator, but the resulting stopping test is not a formal certificate unless those estimates are themselves validated. Under the certified assumptions, we prove that the stopping condition ${max}_{\tau }\eta \left(\tau \right)\le \epsilon$ guarantees ${sup}_{u\in {\Delta }_d}{\parallel z\left(u\right)-\widehat{z}\left(u\right)\parallel }_{\infty }\le \epsilon$, and that the oracle complexity of certified simplicial piecewise-affine reconstruction is $\mathsf{\Theta }\left({\epsilon }^{-d/2}\right)$. On the rigorously certified core tests, CATS uses $2.7\times$–$3.8\times$ fewer oracle calls than uniform reference-direction sampling and $1.2\times$–$1.6\times$ fewer than an AWS-inspired patch-area refinement rule. Additional benchmark studies, evaluated with the same interpolation quantity as a practical stopping indicator, show the same qualitative advantage, especially on anisotropic and localized surface geometries. Full article

In precision gear manufacturing, longitudinal crowning on tooth flanks is commonly produced by applying diagonal feed in worm-type generating processes using tools such as variable-tooth-thickness hobs and dressable grinding worms. However, precise twist control remains difficult because the geometric parameters of the generating tool are strongly coupled with the machine feed settings in the underlying generating kinematics. In addition, direct numerical optimization becomes unreliable near the standard tool state, where the sensitivity of the diagonal-feed coefficient degenerates and conventional linearized solvers may lose effectiveness. To address these issues, this study proposes a multi-variable optimization framework for twist-constrained worm-type gear generation. An iterative singular value decomposition (SVD) scheme is developed to construct and update the sensitivity matrix, while a warm-start continuation strategy is introduced to overcome the local singularity and improve numerical robustness. Two closed-form expressions for the diagonal-feed coefficient are also proposed as practically useful initial estimates, corresponding respectively to the minimum SVD topographic residual and the minimum tooth-flank twist. Numerical validation over a 60-case parameter sweep shows maximum relative errors below 1.6% within the tested range. The proposed framework coordinates the tool-geometry design and diagonal-feed selection to generate tooth flanks with prescribed crowning characteristics while satisfying a specified twist requirement and limiting the required diagonal shift. Numerical examples show that the iterative framework reduces the root-mean-square (RMS) topographic error from 1.14 μm to 0.027 μm relative to the analytical setting of Hsu and Fong. These results indicate that the proposed method provides a reliable computational basis for twist control and process-parameter design in advanced CNC gear generation. From a manufacturing standpoint, because the three design criteria are accessed by adjusting only the diagonal-feed ratio on the machine, a single generating-tool design can serve a range of crowning and twist requirements without retooling, reducing setup and tooling efforts in production. Full article

With the rapid advancement of artificial intelligence in recent years, its adoption in structural design has been increasing. However, supervised learning in engineering design is often limited by the availability and quality of labeled data. To address this issue, this study proposes a context-conditioned deep reinforcement learning (DRL) method for the automated optimization of space frame structures, termed the Space Frame Optimization Agent (SFO-Agent). Instead of learning a policy for a single fixed design task, the proposed agent is conditioned on task-related context variables and trained through direct interaction with a finite element analysis environment, thereby avoiding reliance on pre-collected training datasets. In addition, a piecewise reward function is further formulated using code-prescribed limit values to achieve both structural safety and economic efficiency. Numerical experiments demonstrate that the proposed agent effectively captures reusable design policies under varying task contexts and produces high-quality, code-compliant designs across the full parameter domain. Comparisons with conventional optimization algorithms further demonstrate its superior efficiency and competitive optimization performance, indicating strong potential for broad engineering applications. Full article

Background: Pulpitis is a common cause of dental pain in children and is primarily an inflammatory condition that can be effectively managed with local operative treatment. Although antibiotics are indicated only in cases of systemic involvement or infection spread, they are frequently overprescribed in dental practice. This misuse contributes to antimicrobial resistance and adverse health outcomes. This systematic review aimed to evaluate antibiotic prescribing practices for pulpitis in pediatric patients and to assess adherence to current clinical guidelines. Methods: A systematic review was conducted in accordance with the PRISMA 2020 statement and registered in PROSPERO (CRD420261342269). A comprehensive search was performed in PubMed/MEDLINE, Scopus, and Embase up to March 2026. Observational studies assessing antibiotic prescribing practices among pediatric dentists were included. A meta-analysis of proportions was conducted using a random-effects model. Risk of bias was assessed using the Joanna Briggs Institute Checklist, and certainty of evidence was evaluated using the GRADE approach. Results: Five cross-sectional studies were included. Antibiotic prescribing rates for pulpitis ranged from 0.6% to 50.0%. The pooled prevalence of antibiotic prescribing was 14.0% (95% CI: 5.0–33.5%), with high heterogeneity across studies (I² = 95%). Amoxicillin and amoxicillin–clavulanic acid were the most commonly prescribed first-line antibiotics, while clindamycin was the most frequently reported alternative in patients with penicillin allergy. Treatment duration was generally consistent, ranging from 5 to 7 days. Conclusions: Although pediatric dentists tend to prescribe antibiotics more conservatively than general practitioners, inappropriate use remains prevalent, particularly in conditions such as pulpitis where antibiotic therapy is not indicated. These findings highlight a persistent gap between evidence-based recommendations and clinical practice and underscore the need for targeted antimicrobial stewardship strategies to optimize antibiotic use in pediatric dentistry. Full article

Background: Healthcare-associated infections (HAIs) and antimicrobial resistance are major global public health challenges, influenced by patient clinical complexity and prescribing practices. Methods: Three-point prevalence surveys (PPSs) were conducted (P1: November 2024; P2: June 2025; P3: November 2025), involving 456 patients at the University Hospital “San Giovanni di Dio e Ruggi d’Aragona”, Salerno, Italy. Results: The prevalence of HAIs fluctuated between 3.1% (P1) and a peak of 6.1% (P2), before decreasing to 1.9% (P3), correlating with the presence of multidrug-resistant pathogens in critical care areas. The prevalence of antibiotic use remained stable (~48%), with a decrease in carbapenem use (from 12% to 9%). A decline in ‘unknown’ McCabe scores from 24.6% to 6.8% (p < 0.001) was also observed, suggesting an improvement in completeness of prognostic data, although changes in data collection practices may also have contributed to this change. Conclusions: We showed an association between clinical severity, prolonged hospitalization, invasive device use, and infection risk in a single tertiary-care hospital, within an exploratory, cross-sectional framework. Despite high healthcare pressure, improvements were observed in antimicrobial stewardship and clinical surveillance. Future strategies should focus on optimal device management and on extending surveillance activities to medical wards with increasing patient complexity. Full article

This study presents a discrete–continuous flux-guided shape-refinement framework for freeform shell geometries under self-weight. The method evaluates the directional relation between a prescribed support-directed transmission field and the shell surface normal, identifies locally underperforming regions, applies top-down geometric updates, and reconstructs a continuous surface at each step. It is intended as a transparent intermediate stage between intuitive freeform design and high-fidelity structural verification. The framework is demonstrated on nine shell cases with different geometries, support conditions, height ranges, and surface irregularities. Across all the cases, the results show reduced normal-component misalignment and increased tangential alignment relative to the prescribed transmission field. A representative finite-element comparison provides case-specific supporting evidence that under a linear-elastic gravity-load model the refined geometry can reduce deformation and stress levels over large surface regions; however, it does not prove general structural optimality or fully membrane-dominated behavior. Geometric roughness remains a key limitation requiring explicit regularization in future work. The approach is positioned as a lightweight geometric pre-optimization tool for conceptual shell design, rather than as a substitute for equilibrium-based form-finding or detailed structural optimization. Full article

Against the background that efficient energy utilization has become a global focus and the demand for energy conservation and consumption reduction of industrial equipment is increasingly urgent, aiming at the problems of permanent magnet synchronous motors (PMSMs) in actual operation, such as parameter perturbation, time-varying load and control saturation constraints, which lead to decreased operation efficiency, insufficient energy utilization, low trajectory tracking accuracy, slow convergence speed, weak anti-interference ability and poor engineering applicability, this paper proposes a predefined-time convergent guaranteed-performance control strategy to provide technical support for the efficient and stable operation of PMSMs. Firstly, a prescribed performance control structure independent of the initial value is designed, which breaks through the dependence of traditional Prescribed Performance Control (PPC) on initial states and lays a control foundation for efficient energy utilization. Secondly, the traditional reinforcement learning algorithm is improved to overcome its randomness defect, which is used to accurately online estimate the composite time-varying disturbances (including parameter perturbation and time-varying load) during the operation of PMSMs. Furthermore, the predefined-time convergence control mechanism is integrated to design a prescribed performance control law for PMSMs, which ensures that the angular velocity tracking error converges to zero within a pre-specified time, realizes time-optimal control, effectively suppresses the adverse effects caused by various disturbances and control saturation, and improves the motor operation efficiency and energy utilization efficiency. Finally, the effectiveness is verified by simulation. The results show that the strategy can effectively improve the trajectory tracking accuracy of PMSMs, achieve fast convergence within the predefined time, enhance the adaptability of the motor to complex working conditions, and further improve the energy utilization efficiency. Full article

In high-speed machining, linear toolpaths constructed from a series of short line segments are widely used but inevitably introduce tangent and curvature discontinuities at segment junctions, which may cause feedrate fluctuation and contouring error. To address this problem, this study proposes a real-time corner smoothing and feedrate interpolation method based on dual cubic Bézier transition curves and an optimal error assignment model. The main contribution lies in coupling analytical corner rounding with error allocation: the approximation error and maximum curvature of the transition curves are obtained explicitly, while the allowable tolerance is optimally distributed between approximation error and chord error so that the overall trajectory error remains within the prescribed bound. A jerk-limited look-ahead interpolator is then developed through reverse scanning and forward interpolation to satisfy geometric constraints, drive constraints, and feedrate commands. Simulation results for a three-dimensional toolpath show that the approximation error, chord error, and total trajectory error are all constrained within the preset tolerance of 0.05 mm. In the mask-machining case, the proposed method reduces the machining time to 13.9 s, corresponding to reductions of approximately 70% and 25% compared with the method without look-ahead and the method with look-ahead only, respectively. These results indicate that the proposed framework can improve motion smoothness and machining efficiency while maintaining trajectory accuracy. Full article