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Recombinant adeno-associated virus (rAAV) vectors are a promising platform for in vivo gene therapies. However, cost-effective, well-characterized processes necessary to manufacture rAAV therapeutics are challenging to develop without an understanding of how process parameters (PPs) affect rAAV product quality attributes (PQAs). In this work, a central composite orthogonal experimental design was employed to examine the influence of four PPs for transient transfection complex formation (polyethylenimine:DNA [PEI:DNA] ratio, total DNA/cell, cocktail volume, and incubation time) on three rAAV PQAs related to capsid content (vector genome titer, vector genome:capsid particle ratio, and two-dimensional vector genome titer ratio). A regression model was established for each PQA using partial least squares, and a design space (DS) was defined in which Monte Carlo simulations predicted < 1% probability of failure (POF) to meet predetermined PQA specifications. Of the three PQAs, viral genome titer was most strongly correlated with changes in complexation PPs. The DS and acceptable PP ranges were largest when incubation time and cocktail volume were kept at mid-high setpoints, and PEI:DNA ratio and total DNA/cell were at low-mid setpoints. Verification experiments confirmed model predictive capability, and this work establishes a framework for studying other rAAV PPs and their relationship to PQAs.
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http://dx.doi.org/10.1002/bit.28508 | DOI Listing |
JMIR Cancer
September 2025
iCARE Secure Data Environment & Digital Collaboration Space, NIHR Imperial Biomedical Research Centre, London, United Kingdom.
Background: Electronic health records (EHRs) are a cornerstone of modern health care delivery, but their current configuration often fragments information across systems, impeding timely and effective clinical decision-making. In gynecological oncology, where care involves complex, multidisciplinary coordination, these limitations can significantly impact the quality and efficiency of patient management. Few studies have examined how EHR systems support clinical decision-making from the perspective of end users.
View Article and Find Full Text PDFNeuro Endocrinol Lett
September 2025
Manisa Celal Bayar University Faculty of Medicine, Division of Endocrinology and Metabolism, Manisa, Turkey.
Objectives: Empty sella is the herniation of the subarachnoid space into the sella turcica; either secondary to identifiable causes (e.g., surgery or radiotherapy); or spontaneously, which is termed primary empty sella (PES).
View Article and Find Full Text PDFPhys Rev Lett
August 2025
The Hong Kong University of Science and Technology, Department of Physics and Institute for Advanced Study, Clear Water Bay, Hong Kong SAR, China.
Photonic crystals are artificial materials characterized by a photonic band structure that governs the propagation of light waves. The photonic gap was originally introduced to inhibit spontaneous emission and facilitate photon localization. In this essay, I will highlight how, despite the established understanding of photonic crystals, they remain highly relevant today.
View Article and Find Full Text PDFPLoS One
September 2025
Hunan Mingxiang Aviation Technology Co., Ltd., Changsha, Hunan, China.
Flexible spacecraft possess the ability to adapt to complex environments and use energy more efficiently, offering enhanced flexibility and stability in space missions, particularly in tasks with significant external disturbances such as deep space exploration and satellite attitude control. However, vibration suppression in flexible spacecraft remains a critical challenge. This study addresses the problem of vibration suppression in flexible spacecraft systems under external disturbances and input constraints.
View Article and Find Full Text PDFPLoS One
September 2025
School of Design and Art, Hunan University, Changsha, Hunan, China.
This study addresses the limitations of traditional interior space design, particularly the timeliness and uniqueness of solutions, by proposing an optimized design framework that integrates a two-stage deep learning network with a single-sample-driven mechanism. In the first stage, the framework employs a Transformer network to extract multi-dimensional features (such as spatial layout, color distribution, furniture style, etc.) from input space images, generating an initial feature vector.
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