Dengue, an Aedes mosquito-borne viral infection, is on the rise with climate and demographic change. In 2023, WHO declared dengue its highest grade of emergency, following the largest number of cases and deaths in recorded history. This emergency highlighted the absence of safe and effective dengue-specific treatments as a gap in the product landscape for care for people with dengue. To accelerate development of dengue-specific treatments for wide implementation and access in all dengue-endemic countries, WHO conducted a landscape analysis of the dengue therapeutics pipeline and convened a global expert and public consultation to develop target product profiles for treatments for non-severe and severe dengue. These target product profiles provide strategic guidance for product developers, regulators, procurement agencies, and funders on the intended use, target populations, and key product characteristics of dengue-specific treatments required to meet public health needs, setting clear targets to drive development of dengue drugs accessible to those who need them the most.
Muscular fatigue can significantly impair joint stability, alter proprioception, and increase injury risk in multidirectional sports such as football. While superficial electromyography (sEMG) is widely used for fatigue detection, signal non-stationarity during dynamic movements limits the reliability of traditional methods. This study assessed the potential of complexity-based sEMG features in capturing myoelectric manifestations of fatigue during a dynamic task incorporating sport-related movement patterns, while examining variability in lower-limb joint kinematics. Forty-five football players were monitored using a synchronised wearable system comprising sEMG and Inertial Measurement Units (IMUs) during a running activity with unpredictable directional changes. Spectral sEMG features (Median Frequency and Instantaneous Median Frequency) were compared with complexity-based indices (Sample Entropy, SE; Permutation Entropy) across six muscles. Results suggest that SE may offer enhanced robustness against motion-related non-stationarity, exhibiting a progressive decrease across muscles. Specifically, the most consistent fatigue-related adaptations were observed in the soleus and the vastus medialis (p < 0.001). Concurrently, alterations in joint motion variability patterns were observed in the hip, knee, and ankle during the running cycle, which may reflect fatigue-induced adaptations. These findings suggest that SE is a promising indicator for monitoring neuromuscular adjustments in dynamic conditions. Furthermore, exploring the co-occurrence between complexity-based sEMG trends and IMU-derived kinematics offers a valuable approach for contextualising adaptations in neuromuscular control during a challenging dynamic task. Algorithmic estimates suggest the theoretical feasibility of near-real-time computation, though full system validation in real-world sports environments remains a prerequisite for formally recommending coaching applications or injury-prevention strategies.
We introduce and discuss a kinetic framework describing the time evolution of the statistical distributions of a population divided into the compartments of susceptible, infectious, recovered, and resistant in the presence of a microbial infection driven by susceptible–infectious interactions. Our main objective is to quantify the impact of excessive and inappropriate antimicrobial use, which accelerates the spread of resistance by enabling a fraction of infectious individuals to transition into the resistant compartment. The model consists of a system of Boltzmann-type equations capturing binary interactions between susceptible and infectious individuals, complemented by linear redistribution operators that represent recovery, the development of resistance, and reinfection processes. In the grazing-collision limit, we show that this Boltzmann system is well approximated by a system of coupled Fokker–Planck equations. This limiting description allows for a more tractable analysis of the dynamics, including the characterization of the long-time behavior of the population densities. Our analysis highlights how interaction terms drive the system toward a stable equilibrium and quantifies the effects of inappropriate antimicrobial use on the distribution of resistant individuals. Overall, the results offer a multi-scale perspective that bridges kinetic theory with classical epidemic modeling.
Natural Gradient Descent (NGD) has emerged as a promising optimization algorithm for training neural network-based solvers for partial differential equations (PDEs), such as Physics-Informed Neural Networks (PINNs). However, its practical use is often limited by the high computational cost of solving linear systems involving the Gramian matrix. While matrix-free NGD methods based on the conjugate gradient (CG) method avoid explicit matrix inversion, the ill-conditioning of the Gramian significantly slows the convergence of CG. In this work, we extend matrix-free NGD to broader classes of problems than previously considered and propose the use of Randomized Numerical Linear Algebra (RandNLA) techniques for efficient preconditioning of the inner CG solver. The resulting algorithms demonstrate substantial performance improvements over existing NGD-based methods on a range of PDE problems discretized using neural networks, and offer competitive results compared to other state-of-the-art optimizers.
Childhood cancer survivors (CCSs) experience a premature onset of ageing-related morbidities, including cancer-related cognitive impairment (CRCI), attributable to epigenetic age acceleration (EAA). The National Cancer Institute (NCI) recommended non-pharmacological strategies (NPIs) as the optimal approach. To address current gaps in the literature, we conducted a systematic review of RCTs across six databases to provide a comprehensive overview of NPIs for managing CRCI in CCSs and adolescents, following the Cochrane Handbook for Systematic Reviews of Interventions and the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement. Thirteen publications and eleven RCTs were included, including two quasi-experimental and six pilot and feasibility studies. The sample included individuals aged 4–17 years with solid brain and haematological tumours. Strategies included academic skills interventions, cognitive-motor training with digital and analogue systems, computerised and non-computerised cognitive training, web-based physical activity, and physical activity with active video gaming or exergaming. The results suggest improvements in intellectual function and cognitive abilities following structured cognitive training programs. The integration of these interventions in a coordinated survivorship care plan across healthcare, educational, and family systems will support the cognitive development and adaptive outcomes to reduce learning and social disparities in children and adolescents with cancer. Further RCTs are needed to allow for rigorous quantitative data synthesis and to target specific age groups.
