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Factor VIIIa (LongAte)

✓ Approved

PolyTherics Limited · F8 · 重组蛋白

什么是 Factor VIIIa?

Factor VIIIa 是一种重组蛋白,由PolyTherics Limited研发。该药已获批,用于治疗相关适应症,给药途径:Injectable (Others)、Intravenous (IV)、Subcutaneous Injection。

药物档案

商品名LongAte
公司PolyTherics Limited
药物类别重组蛋白, 细胞治疗
分子靶点F8
给药途径Injectable (Others), Intravenous (IV), Subcutaneous Injection
状态Approved

作用机制

分子靶点

Factor VIIIa 作用于 1 个分子靶点:

F8coagulation factor VIII (AHF, FVIII)
需要更深入的分析?Noah AI 可解释复杂机制并与同类药物比较。

治疗适应症

Factor VIIIa 针对 1 个适应症,涉及 1 个治疗领域。

治疗领域疾病/病症分期
Congenital, familial and genetic disordersFactor VIII deficiency✓ Approved

相关研究文献

PubMedGels (Basel, Switzerland)2026-07-27

An Ultrasound-Responsive Bio-Adhesive Piezoelectric Hydrogel for Osteoarthritis Cartilage.

Li Yuan Y, Chen Ziyu Z, Zhu Shiyu S, Wei Yan Y et al.

Osteoarthritis (OA) is a degenerative joint disease characterized by progressive loss of articular cartilage and an associated decline in its intrinsic mechanoelectrical signaling. Current osteoarthritis treatments relieve symptoms but fail to prevent cartilage degeneration or restore its native biophysical microenvironment. Here, we present an ultrasound-activated, mussel-inspired bio-adhesive hydrogel that addresses these challenges by recreating the cartilage's piezoelectric cues in situ while achieving stable intra-articular retention under synovial conditions. The hydrogel, denoted SFHD-BT@PDA, consists of a silk fibroin (SF) matrix integrated with dopamine-functionalized hyaluronic acid (HADA) and embedded barium titanate nanoparticles coated with polydopamine (BT@PDA). This multi-level design imparts strong interfacial adhesion to wet cartilage (via catechol-mediated bonding to collagen) and piezoelectric sensitivity to external ultrasound. Under ultrasound stimulation, SFHD-BT@PDA generates localized electrical microcurrents that recruit endogenous MSCs via electrotaxis and subsequently promote their chondrogenic differentiation. In vitro, ultrasound-triggered electrical cues upregulated chondrogenic markers (SOX9, collagen II, aggrecan) in MSCs and activated TGF-β signaling, demonstrating restoration of the pro-anabolic bioelectric microenvironment. In a murine DMM model, the adhesive hydrogel exhibited prolonged retention on cartilage surfaces and, with ultrasound, induced robust cartilage regeneration and OA reversal. Treated joints showed preserved proteoglycan and Type II collagen content, inhibited osteophyte formation, and protection of subchondral bone microarchitecture. In summary, this mussel-inspired piezoelectric hydrogel provides an electromechanical stimulation platform that effectively couples physical cues with bio-adhesion to regenerate cartilage.

PMID 42505312
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PubMedInorganic chemistry2026-07-27

Structure and Compressional Behavior of Sillén-Type (BiO)2(CO3) Bismutite Carbonate.

Chuliá-Jordán Raquel R, Santamaría-Pérez David D, Botan-Neto Benedito Donizeti BD, Bera Ganesh G et al.

In this joint experimental/computational work, we study the ambient-conditions structure of bismutite (BiO)2(CO3) and its evolution under high pressure by means of a combination of in situ synchrotron X-ray diffraction (XRD) measurements, Raman spectroscopy, and density-functional theory (DFT) calculations. Using single-crystal XRD, (BiO)2(CO3) is determined to be an orthorhombic Cmcm phase with disorder arising from carbonate rotations at ambient-conditions and DFT confirms the stability of the structure. In situ high-pressure synchrotron powder XRD measurements showed that the Cmcm ambient-pressure structure adequately accounts for the observed diffraction patterns up to 17.7 GPa. The compressibility was determined, obtaining an experimental bulk modulus of 67.2(11) GPa and significant anisotropy in the axial compressibilities: the a and c axes, parallel to the (BiO)22+ layers, are less compressible than the b direction perpendicular to those layers, which we justify on the basis of the bismuth lone electron pair compressibility and the slight reorientation of the carbonate groups. Raman spectroscopy measurements at ambient conditions and under pressure were used to characterize the vibrational properties of this carbonate, unvealing a differentiated behavior of the nonequivalent carbonate units.

PMID 42503691
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PubMedBiomimetics (Basel, Switzerland)2026-07-27

Machine Learning for Graduation Prediction in Higher Education: A Systematic Review with a Bio-Inspired Optimization Perspective.

Yáñez Andrés A, Crawford Broderick B, Monfroy Eric E, Paz Álex Á et al.

Timely graduation, time-to-degree, and degree completion are key indicators of student progression and institutional effectiveness in higher education. This study presents a PRISMA-based systematic literature review of machine learning approaches for graduation-related prediction, with attention to predictive targets, pipeline components, scalability, and bio-inspired optimization. Searches in Web of Science Core Collection and Scopus identified 278 records, of which 25 studies published between 2021 and 2025 met the eligibility criteria. The findings show that most studies formulated graduation prediction as a supervised classification task, relied heavily on academic performance variables, and frequently used tree-based or ensemble models. Feature selection, explainability, and hyperparameter optimization were commonly reported, but bio-inspired optimization was actively implemented in only two studies through Particle Swarm Optimization, Genetic Algorithms, or Ant Colony Optimization. The evidence base also remains limited in scalability, as most studies used single-institution datasets and provided little external validation. These findings identify an opportunity for Bio-Inspired Educational Analytics through scalable feature selection, efficient hyperparameter optimization, model simplification, and multi-objective trade-off analysis. Future research should evaluate whether lightweight, hybrid, and multi-objective metaheuristics can support accurate, interpretable, fair, and transferable graduation prediction systems.

PMID 42505545
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PubMedBiomimetics (Basel, Switzerland)2026-07-27

Fly-by-Feel: Advancements and Applications of Bio-Inspired Wind-Hair Sensors on Fixed-Wing UAVs.

Selim Omar O, Court Alecsandra A, Brücker Christoph C

Distributed aerodynamic sensing is a key requirement for future fly-by-feel UAV systems. Inspired by mechanosensory systems found in flying animals, this paper investigates the use of a bio-inspired optically tracked flexible pillar sensor array for aerodynamic sensing and stall detection on a washed-out NACA0012 aerofoil. Experiments were conducted in a low-speed water tunnel, with flow at chord-based Reynolds Re=70×103 and the pillar sensors set to measure local flow conditions. Sensor calibration and dynamic characterisation were performed prior to testing. Time-resolved flow visualisation measurements were used to validate sensor response and investigate local flow phenomena. The results demonstrated that flexible pillar sensors can capture early indications of stall through monitoring of spanwise mean deflection, flow reversal events associated with incipient and fully separated flow, and characteristic low-frequency oscillations. The findings demonstrate the potential of distributed bio-inspired sensor arrays to enhance stall detection and enable real-time aerodynamic monitoring in future fly-by-feel UAV systems.

PMID 42505533
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PubMedBiomimetics (Basel, Switzerland)2026-07-27

Adaptive Digital Marketing: A Systematic Review of Bio-Inspired Reinforcement Learning, Multi-Agent Systems, and Agentic AI for Intelligent Optimisation.

Adhikari Tek Narayan TN, Sayers William W, Zhang Shujun S

Digital marketing increasingly functions as a complex adaptive system characterised by non-stationary environments, strategic interaction, and multi-agent competition. Programmatic advertising exemplifies this complexity, where decisions must be made in real time under uncertainty. Under such conditions, traditional static optimisation methods often fail to deliver robust performance. This review synthesises bio-inspired computational approaches, reinforcement learning (RL), multi-agent reinforcement learning (MARL), and agentic artificial intelligence (AI) to develop an integrated theoretical perspective on adaptive optimisation in digital marketing. Following PRISMA 2020 guidelines, we conducted a systematic search of peer-reviewed research across six databases: Scopus, IEEE Xplore, ACM Digital Library, SpringerLink, ScienceDirect, and arXiv, supplemented by manual reference checking. Each computational paradigm is explicitly grounded in foundational biological literature, including work on evolution, foraging, swarm intelligence, and immune cognition. Reinforcement learning supports adaptive decision-making through mechanisms closely aligned with operant conditioning and foraging behaviour. Multi-agent reinforcement learning extends these principles to interactive marketing ecosystems via decentralised coordination and swarm-based learning. Agentic AI further advances adaptive capability by introducing goal-directed reasoning, memory, and higher-level decision orchestration. Contributions: The review identifies persistent fragmentation across marketing sub-domains and a lack of formal mathematical grounding for widely used bio-inspired analogies. To address these gaps, the study proposes a multi-layer bio-inspired framework and outlines a structured research agenda to guide the development of autonomous digital marketing systems.

PMID 42505509
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PubMedBiomimetics (Basel, Switzerland)2026-07-27

Bio-Inspired Explainable Evolutionary Rule Mining for Thermodynamic Performance Assessment of a Solar Greenhouse Dryer.

Das Mehmet M, Akpinar Ebru E, Dogan Ferdi F, Pektezel Oguzhan O et al.

This study investigates the thermodynamic and drying performance of a greenhouse dryer integrated with a parabolic trough solar collector (PTSC) and develops interpretable operating rules using a bio-inspired explainable artificial intelligence framework. Outdoor apple-drying experiments were conducted, and system performance was evaluated in terms of energy, drying, and exergy efficiencies. The experimental results indicated that energy efficiency ranged from 17.7% to 29.2%, drying efficiency from 1.0% to 9.7%, and exergy efficiency from 5.6% to 8.4%. Measured variables, including temperature, relative humidity, product weight, and solar radiation, were used to classify the efficiencies into low, medium, and high categories using the Chaotic Rule-based Strength Pareto Evolutionary Algorithm 2 (CRb-SPEA2). As a bio-inspired evolutionary computing approach, CRb-SPEA2 employs population-based search, selection, Pareto dominance, and multi-objective optimization mechanisms inspired by natural evolutionary processes. In contrast to conventional black-box machine learning models, the proposed method extracts explicit decision rules that define physically meaningful operating ranges. The maximum recall values were 0.952, 1.000, and 0.971 for the high-energy-, drying-, and exergy-efficiency classes, respectively. The extracted rules identified solar radiation, temperature, relative humidity, and product weight as dominant factors affecting dryer performance.

PMID 42505511
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