Search Results (5,620)

Biopharmaceutical medicines represent one of the most dynamic sectors of the pharmaceutical industry, with therapeutic proteins forming the largest and most important group. Their structural complexity and inherent sensitivity to chemical and physical stressors, however, continue to pose major challenges for formulation development and long-term stability. Short peptides have emerged as a promising yet underutilized class of excipients for protein-based drug products. Their modular architecture allows for precise tuning of physicochemical properties such as polarity, charge distribution, and hydrogen-bonding potential, thereby offering advantages over single amino acids. Experimental studies indicate that short peptides can serve multiple functions: stabilizers, antioxidants, viscosity-lowering agents, and as lyo/cryoprotectants or bulking agents in lyophilized formulations. Notably, the relatively small and chemically defined space of short peptides—approximately 400 possible dipeptides and 8000 tripeptides—makes them particularly amenable to systematic screening and computational modeling. This enables rational identification of candidates with tailored excipient functions. This review summarizes current knowledge on the use of short peptides as excipients in parenteral protein formulations, with a focus on their functional versatility and potential for rational design in future development. Full article

Content Delivery Networks (CDNs) have evolved to meet surging data demands and stringent low-latency requirements driven by emerging applications like high-definition video streaming, virtual reality, and IoT. This paper proposes a hybrid CDN architecture that synergistically combines edge caching, Multi-access Edge Computing (MEC) offloading, and reinforcement learning (Q-learning) for adaptive routing. In the proposed system, popular content is cached at radio access network edges (e.g., base stations) and computation-intensive tasks are offloaded to MEC servers, while a Q-learning agent dynamically routes user requests to the optimal service node (cache, MEC server, or origin) based on the network state. The study presented detailed system design and provided comprehensive simulation-based evaluation. The results demonstrate that the proposed hybrid approach significantly improves cache hit ratios and reduces end-to-end latency compared to traditional CDNs and simpler edge architectures. The Q-learning-enabled routing adapts to changing load and content popularity, converging to efficient policies that outperform static baselines. The proposed hybrid model has been tested against variants lacking MEC, edge caching, or the RL-based controller to isolate each component’s contributions. The paper concludes with a discussion on practical considerations, limitations, and future directions for intelligent CDN networking at the edge. Full article

Objective: This study evaluated the cost-effectiveness of sacituzumab govitecan (SG) compared with single-agent chemotherapy of the physician’s choice (TPC) from the perspective of Taiwan’s National Health Insurance. Methods: A partitioned survival model was developed to assess outcomes in patients with metastatic triple-negative breast cancer (mTNBC). Clinical data were derived from the ASCENT trial, while direct medical costs were obtained from Taiwan’s National Health Insurance Administration (NHIA). Utility values were taken from published literature. The primary outcome was the incremental cost-effectiveness ratio (ICER), expressed as cost per quality-adjusted life year (QALY) gained. One-way and probabilistic sensitivity analyses were performed to examine parameter uncertainty and test the robustness of the results. Results: In the base-case analysis, SG was associated with an incremental cost of USD 121,836 per QALY gained—exceeding Taiwan’s willingness-to-pay (WTP) threshold of USD 102,120. One-way sensitivity analyses indicated that SG drug cost was the primary driver of ICER variability. Probabilistic sensitivity analysis showed that reducing the price of SG by 50% increased the likelihood of cost-effectiveness. Conclusions: From the NHIA perspective, SG is not cost-effective for patients with advanced or metastatic TNBC at its current price. Substantial price reductions would be required for SG to become cost-effective under the WTP threshold of USD 102,120 per QALY. Full article

Residual cardiovascular risk remains substantial despite widespread adoption of intensive lipid-lowering strategies—statins, PCSK9 inhibitors, and RNA-based agents—that achieve very low LDL-C and apoB levels. Over the past three years, converging epidemiologic and mechanistic evidence has highlighted emotional stress—including anger, grief, anxiety, and chronic psychosocial strain—as a biologically active determinant of atherosclerotic disease and a frequent trigger of acute events. We propose the Emotion–Lipid Synergy Model, in which lipid burden establishes the atherothrombotic substrate while emotion-driven autonomic and vascular perturbations amplify endothelial dysfunction, microvascular constriction, inflammation, and thrombogenicity—thereby widening the residual-risk gap even when lipid targets are met. From this perspective, prevention should evolve toward precision psychocardiology: systematically screening for distress and stress reactivity; leveraging wearables to detect high-risk emotional states; and delivering timely, scalable, just-in-time behavioral interventions alongside guideline-directed lipid management. Particular attention is warranted for women and patients with angina and no obstructive coronary disease, who appear disproportionately susceptible to mental-stress ischemia. We outline a research agenda—flagship outcomes trials, mechanistic studies, and multimodal phenotyping—and discuss implementation pathways that integrate emotion metrics into cardiac rehabilitation and routine care. Integrating emotion assessment and modulation with lipid control offers a pragmatic route to reduce residual risk and advance equitable, personalized cardiovascular prevention. Full article

The rapid proliferation of residential electric vehicles (EVs), rooftop photovoltaics (PVs), and behind-the-meter batteries is transforming energy communities while introducing new operational stresses to local distribution grids. Short-duration transformer overloads, often overlooked in conventional hourly or optimization-based planning models, can accelerate asset aging before voltage limits are reached. This study introduces a second-by-second, multi-agent-based simulation (MABS) framework that couples empirically calibrated Distributed Energy Resource (DER) adoption trajectories with real-time-price (RTP)–driven household charging decisions. Using a real 160-household feeder in Denmark (2024–2025), five progressively integrated DER scenarios are evaluated, ranging from EV-only adoption to fully synchronized EV–PV–battery coupling. Results reveal that uncoordinated EV charging under RTP shifts demand to early-morning hours, causing the first transformer overload within four months. PV deployment alone offers limited relief, while adding batteries delays overload onset by 55 days. Only fully coordinated EV–PV–battery adoption postponed the first overload by three months and reduced total overload hours in 2025 by 39%. The core novelty of this work lies in combining empirically grounded adoption behavior, second-level temporal fidelity, and agent-based grid dynamics to expose transient overload mechanisms invisible to coarser models. The framework provides a diagnostic and planning tool for distribution system operators to evaluate tariff designs, bundled incentives, and coordinated DER deployment strategies that enhance transformer longevity and grid resilience in future energy communities. Full article

We present a hybrid partial differential equation-agent-based model (PDE-ABM). In our framework, tumor cells secrete tumor angiogenic factor (TAF), while endothelial cells chemotactically migrate and branch in response. Reaction–diffusion PDEs for TAF, oxygen, and cytotoxic drug are coupled to discrete stochastic dynamics of tumor cells and endothelial tip cells, ensuring multiscale integration. Motivated by observed perfusion heterogeneity in tumors and its pharmacokinetic consequences, we conduct a linear stability analysis for a reduced endothelial–TAF reaction–diffusion subsystem and derive an explicit finite-domain threshold for Turing instability. We demonstrate that bidirectional coupling, where endothelial cells both chemotactically migrate along TAF gradients and secrete TAF, is necessary and sufficient to generate spatially periodic vascular clusters and inter-cluster hypoxic regions. These emergent patterns produce heterogeneous drug penetration and resistant niches. Our results identify TAF clearance, chemotactic sensitivity, and endothelial motility as effective levers to homogenize perfusion. The model is two-dimensional and employs simplified kinetics, and we outline necessary extensions to three dimensions and saturable kinetics required for quantitative calibration. The study links reaction–diffusion mechanisms with clinical principles and suggests actionable strategies to mitigate resistance by targeting endothelial–TAF feedback. Full article

The filarioid nematode Dirofilaria immitis is the causative agent of heartworm disease, a major parasitic infection of canids, felids and occasionally humans. Current prevention relies on macrocyclic lactone-based chemoprophylaxis, but the emergence of drug resistance highlights the need for new intervention strategies. Here, we applied a machine learning (ML)-based framework to predict and prioritise essential genes in D. immitis in silico, using genomic, transcriptomic and functional datasets from the model organisms Caenorhabditis elegans and Drosophila melanogaster. With a curated set of 26 predictive features, we trained and evaluated multiple ML models and, using a defined threshold, we predicted 406 ‘high-priority’ essential genes. These genes showed strong transcriptional activity across developmental stages and were inferred to be enriched in pathways related to ribosome biogenesis, translation, RNA processing and signalling, underscoring their potential as anthelmintic targets. Transcriptomic analyses suggested that these genes are associated with key reproductive and neural tissues, while chromosomal mapping revealed a relatively even genomic distribution, in contrast to patterns observed in C. elegans and Dr. melanogaster. In addition, initial evidence suggested structural variation in the X chromosome compared with a recently published D. immitis assembly, indicating the importance of integrating long-read sequencing with high-throughput chromosome conformation capture (Hi-C) mapping. Overall, this study reinforces the potential of ML-guided approaches for essential gene discovery in parasitic nematodes and provides a foundation for downstream validation and therapeutic target development. Full article

Glioblastoma multiforme (GBM) is a common malignancy with poor prognosis, and exhausted T (TEX) cells, a subset of T cells characterized by progressive loss of effector functions, play a critical role in its progression. This study aimed to investigate the impact of TEX-related genes on immune function, prognosis, and drug sensitivity in GBM through multiomics analysis. Initially, we identified a novel set of TEX-related genes specific to GBM and screened hub genes (CCL5, IL18, CXCR6, FCER1G, TNFSF13B) using conventional statistical methods combined with machine learning. A prognostic risk model was subsequently constructed based on TCGA data and validated in the CGGA cohort. Single-cell and pharmacogenomic analyses revealed significant differences in tumor microenvironment composition and drug sensitivity between risk groups. Notably, Palbociclib emerged as a potential therapeutic agent targeting the novel discovered biomarker CCL5. RT-qPCR results showed that T cells with low CCL5 expression exhibited reduced expression of immune checkpoint-related genes (PD1, TIM3, LAG3) and increased expression of CD28, suggesting enhanced immune function. In conclusion, our findings highlight five hub genes as prognostic markers that could stratify GBM patients with different immune landscapes and levels of drug sensitivity. Furthermore, experimental results suggest that low CCL5 expression could alleviate T cell exhaustion and represent a promising therapeutic target, offering new strategies for improving GBM prognosis. Full article

Background: The emergence of pan-drug-resistant (PDR) Klebsiella pneumoniae has compromised the efficacy of last-line agents, leaving few therapeutic options. Repurposing zidovudine, an FDA-approved thymidine analog with antibacterial activity, may enhance existing therapies, but pharmacodynamic data under clinically relevant conditions are scarce. This study addresses this gap using static and dynamic in vitro models. Materials/methods: A PDR strain of Klebsiella pneumoniae harboring bla_NDM-1, bla_CMY-6, bla_CTX-M-15, bla_SHV-2, and disrupted mgrB was used in this study. Minimum inhibitory concentrations (MICs) followed by static time-kills were performed to investigate the synergistic interplay between zidovudine and last-line antibiotics (ceftazidime/avibactam, polymyxin B). To simulate human pharmacokinetics, a hollow-fiber infection model (HFIM) was employed using steady-state concentrations of zidovudine (4 mg/L), polymyxin B (4 mg/L), and avibactam (22 mg/L). Structural and morphological effects on bacterial cells were examined via fluorescence microscopy following glutaraldehyde fixation. Results: In this study, the PDR K. pneumoniae showed a ~5-fold reduction in polymyxin MIC when combined with zidovudine (from >4 µg/mL to 0.25 µg/mL). Time-kill assays demonstrated ≥2.5 log₁₀ CFU/mL bacterial reduction with zidovudine-based combinations, whereas monotherapies failed to inhibit bacterial growth. In the HFIM, the triple combination achieved rapid bactericidal activity (>3 log₁₀ CFU/mL reduction within 4 h) and sustained killing (>5–6 log₁₀ reduction maintained through 216 h), with bacterial counts remaining below 1 CFU/mL. In contrast, dual combinations initially reduced bacterial burden (1–3 log₁₀ reduction) but failed to maintain suppression, with significant regrowth (>10¹⁰ CFU/mL) observed by 168 h. Microscopy corroborated these findings, revealing extensive cellular damage in the zidovudine-containing treatment arms. These HFIM results underscore the potential of zidovudine-based triple therapy in overcoming resistance to last-line antibiotics in K. pneumoniae. Conclusions: Our results provide promising unprecedented insight into novel zidovudine-based combination therapies against difficult-to-treat MBL Gram-negatives. The observed synergy in MIC reduction, rapid killing in time-kill assays, and near-complete eradication in the HFIM underscore the therapeutic potential of this triple combination. Future studies will focus on broadening the application of these novel combinations to other ‘superbugs’, such as highly resistant strains of Acinetobacter baumannii and Pseudomonas aeruginosa. Full article

Conventional separation methods often prove ineffective for complex, refractory high-phosphorus iron ores. Recent advances propose a coal-based direct reduction dephosphorization-magnetic separation process, achieving significant dephosphorization efficiency. This review systematically analyzes phosphorus occurrence states in high-phosphorus oolitic iron ores across global deposits, particularly within iron minerals. We categorize contemporary research and elucidate dephosphorization mechanisms during coal-based direct reduction. Key factors influencing iron mineral phase transformation, iron enrichment, and phosphorus removal are comprehensively evaluated. Phosphorus primarily exists as apatite and collophane gangue m horization agents function by: (1) inhibiting phosphorus-bearing mineral reactions or binding phosphorus into soluble salts to prevent incorporation into metallic iron; (2) enhancing iron oxide reduction and coal gasification; (3) disrupting oolitic structures, promoting metallic iron particle growth, and improving the intergrowth relationship between metallic iron and gangue. Iron mineral phase transformations follow the sequence: Fe₂O₃ → Fe₃O₄ → FeO (FeAl₂O₄, Fe₂SiO₄) → Fe. Critical parameters for effective dephosphorization under non-reductive phosphorus conditions include reduction temperature, duration, reductant/dephosphorization agent types/dosages. Future research should focus on: (1) investigating phosphorus forms in iron minerals for targeted ore utilization; (2) reducing dephosphorization agent consumption and developing sustainable alternatives; (3) refining models for metallic iron growth and improving energy efficiency; (4) optimizing reduction atmosphere control; (5) implementing low-carbon emission strategies. Full article

Understanding adsorption and interfacial properties of surface-active agents at interfaces is crucial to the formation and stability of colloidal systems such as emulsions and foams. In this work, interfacial tension and viscoelasticity of saponin at the β-pinene/water interface were studied using drop tensiometry and dilational rheology measurement. For comparison, saponin at the air/water interface was also evaluated. Both saponin and β-pinene are bio-based, eco-friendly, and abundant in plants, trees, and agricultural wastes. Results showed that dynamic interfacial tensions σ(t) of saponin adsorbed at β-pinene/water and air/water interfaces could be well described by the Ward and Tordai model, suggesting that the saponin adsorption kinetics at both interfaces are controlled by a kinetically limited mechanism. The equilibrium interfacial pressure π_e data prior to critical micelle concentration (cmc) were adequately fitted by the Gibbs adsorption isotherm. At the β-pinene/water interface, a higher cmc and a larger area per molecule, but a lower π_e, were observed compared to the air/water interface. Interestingly, the dilational moduli of saponin at β-pinene/water increased with increasing oscillating frequency, but with less significant frequency dependence than their counterparts at the air/water interface. The dilational moduli of saponin at β-pinene/water passed through a minimum with increasing saponin bulk concentration, while the air/water interface exhibited a strikingly different trend in terms of concentration dependence and a higher magnitude for the dilational moduli. The correlation between adsorption behaviors and dilational properties of saponin at the two interfaces is discussed. Fundamental knowledge gained from this study will be beneficial for the rational development of new biocompatible emulsions and foam products for more sustainable applications. Full article

Objectives: As a known antioxidant, hydrogen has been useful for treating pressure ulcers. However, conventional methods of hydrogen administration have limitations with regard to dosage and continuity of hydrogen intake. This study evaluated the efficacy of a novel Si-containing agent that can generate substantial quantities of hydrogen to treat pressure ulcers in an in vivo mouse model. Methods: The back skin and subcutaneous tissue of mice were compressed with magnets for 12 h. Changes in the ulcer area after release of compression, histological findings, degree of apoptosis, and expression levels for oxidative stress markers and inflammation-related cytokines were compared between mice fed a normal diet (control group) and those fed a 2.5 wt% Si-based diet (Si group). Results: The Si group had a significantly smaller ulcer area and shorter healing period than the control group. Moreover, inflammatory responses, apoptotic activity, and oxidative stress within the ulcer tissue were suppressed significantly in the Si group. Conclusions: Oral intake of the Si-based agent can potentially treat and prevent pressure ulcers by regulating apoptosis, oxidative stress, and inflammatory responses. Full article

This paper develops a Natural Language Processing (NLP) pipeline to quantify the hawkish–dovish stance in the Federal Reserve’s semiannual Monetary Policy Reports (MPRs). The goal is to transform long-form central-bank text into reproducible stance scores and interpretable policy signals for research and monitoring. The corpus comprises 26 MPRs (26 February 2013 to 20 June 2025). PDFs are parsed and segmented and chunks are embedded, indexed with FAISS, retrieved via LangChain, and scored by GPT-4o on a continuous scale from −2 (dovish) to +2 (hawkish). Reliability is assessed with a four-dimension validation suite: (i) semantic consistency using cosine-similarity separation, (ii) numerical consistency against theory-implied correlation ranges (e.g., Taylor-rule logic), (iii) bootstrap stability of reported metrics, and (iv) content-quality diagnostics. Results show a predominant Neutral distribution (50.0%), with Dovish (26.9%) and Hawkish (23.1%). The average stance is near zero (≈0.019) with volatility σ ≈ 0.866, and the latest window exhibits a hawkish drift of ~+0.8 points. The Numerical Consistency Score is 0.800, and the integrated validation score is 0.796, indicating publication-grade robustness. We conclude that an embedding-based, agentic RAG approach with GPT-4o yields a scalable, auditable measure of FED communication; limitations include biannual frequency and prompt/model sensitivity, but the framework is suitable for policy tracking and empirical applications. Full article

Agile satellites leverage rapid and flexible maneuvering to image more targets per orbital cycle, which is essential for time-sensitive emergency operations, particularly disaster assessment. Correspondingly, the increasing observation data volumes necessitate the use of on-orbit computing to bypass storage and transmission limitations. However, coordinating precedence-dependent observation, computation, and downlink operations within limited time windows presents key challenges for agile satellite service optimization. Therefore, this paper proposes a deep reinforcement learning (DRL) approach to solve the joint observation and on-orbit computation scheduling (JOOCS) problem for agile satellite constellations. First, the infrastructure under study consists of observation satellites, a GEO satellite (dedicated to computing), ground stations, and communication links interconnecting them. Next, the JOOCS problem is described using mathematical formulations, and then a partially observable Markov decision process model is established with the objective of maximizing task completion profits. Finally, we design a joint scheduling decision algorithm based on multiagent proximal policy optimization (JS-MAPPO). Concerning the policy network of agents, a problem-specific encoder–decoder architecture is developed to improve the learning efficiency of JS-MAPPO. Simulation results show that JS-MAPPO surpasses the genetic algorithm and state-of-the-art DRL methods across various problem scales while incurring lower computational costs. Compared to random scheduling, JOOCS achieves up to 82.67% higher average task profit, demonstrating enhanced operational performance in agile satellite constellations. Full article

Although information theory is widely used in neuroscience, its application has primarily been limited to the analysis of neural activity, with much less emphasis on behavioral data. This is despite the fact that the discrete nature of behavioral variables in many experimental settings—such as choice and reward outcomes—makes them particularly well-suited to information-theoretic analysis. In this study, we provide a framework for how behavioral metrics based on conditional entropy and mutual information can be used to infer an agent’s decision-making and learning strategies under uncertainty. Using simulated reinforcement-learning models as ground truth, we illustrate how information-theoretic metrics can reveal the underlying learning and choice mechanisms. Specifically, we show that these metrics can uncover (1) a positivity bias, reflected in higher learning rates for rewarded compared to unrewarded outcomes; (2) gradual, history-dependent changes in the learning rates indicative of metaplasticity; (3) adjustments in choice strategies driven by reward harvest rate; and (4) the presence of alternative learning strategies and their interaction. Overall, our study highlights how information theory can leverage the discrete, trial-by-trial structure of many cognitive tasks, with the added advantage of being parameter-free as opposed to more traditional methods such as logistic regression. Information theory thus offers a versatile framework for investigating neural and computational mechanisms of learning and choice under uncertainty—with potential for further extension. Full article