Scalability of filter throughput in normal flow filtration runs is an important consideration in the development of biopharmaceutical downstream processes. Depending on the filtration mode used, filter device geometry can significantly affect scalability. In this study, scaling of different polyethersulfone sterilizing-grade filters was performed in two filtration modes-at constant flow and at constant pressure-using a particulate model solution as well as a cell-free mAb solution as a representative example. Both filtration methods were compared regarding their practicability as well as their scalability of the final filter throughput and the filtration time. The pressure-dependent filter fouling that occurred with the mAb solution and the model solution showed that using different pressures for small- and process-scale filtration runs could potentially influence the predicted filter capacity. Overall, good scalability of the final filter throughput was determined for filters ranging from small-scale flat disc filters (4.5 cm) to large pleated filter assemblies (5.4 m) in filtration runs at constant flow as well as for filter capsules (0.6 m) of up to 10" for filtration runs at constant pressure. Moreover, at constant flow, the filtration time could be accurately predicted because it was determined by the adjusted flow rate. However, at constant pressure, potential resistances in process-scale devices can result in lower fluid fluxes and, hence, a longer unpredictable filtration time compared with small-scale filter elements. This paper introduces a novel scaling method performed at constant pressure that compensates for the pressure losses resulting from process-scale device resistances. Improved scalability regarding final filter throughput and filtration time are shown here with this scaling method compared with scaling at constant pressure. Therefore, this study provides information essential in decision-making to achieve optimal scaling within biopharmaceutical process development.
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http://dx.doi.org/10.5731/pdajpst.2019.011254 | DOI Listing |
Heliyon
January 2025
Institute of Genomic Medicine Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
Objectives: Alzheimer's disease (AD) is a complex neurodegenerative disorder that primarily affects elderly individuals. This study aimed to elucidate the intricate mechanisms underlying AD in elderly patients compared with healthy aged individuals using high-throughput RNA sequencing (RNA-seq) data and next-generation knowledge discovery methods (NGKD), with a focus on identifying potential therapeutic agents.
Methods: High-throughput RNA-seq data were obtained from the Gene Expression Omnibus (GEO) database (accession number: GSE104704).
JSLS
January 2025
Department of Obstetrics and Gynecology, NYU Langone Health Grossman School of Medicine, New York, New York, USA. (Drs. V. Shah, Munoz, and Huang).
Background And Objectives: Operating rooms (ORs) are critical for hospital revenue and cost management, with utilization efficiency directly affecting financial outcomes. Traditional surgical scheduling often results in suboptimal OR use. We aim to build a machine learning (ML) model to predict incision times for robotic-assisted hysterectomies, enhancing scheduling accuracy and hospital finances.
View Article and Find Full Text PDFJ Agric Food Chem
January 2025
Zhengzhou Tobacco Research Institute of CNTC, Fengyang Street #2, Zhengzhou, Henan 450001, PR China.
The occurrence of off-flavor in osmanthus absolutes has emerged as a significant concern that could hinder its broad market acceptance and associated economic development. In this study, key off-flavor molecules in industrial osmanthus absolute were identified through sensomics and chemometric approaches. A group of 10 off-flavor (OF) samples, eliciting smoky/phenolic, sweaty/sour, and spicy odors, were compared with 10 pleasant aroma (PA) samples through various analyses, including overall aroma assessment, comprehensive chemical profiling, aroma extract dilution analysis (AEDA), and orthogonal partial least-squares-discriminant analysis (OPLS-DA).
View Article and Find Full Text PDFJ Neural Eng
January 2025
Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania, 15213, UNITED STATES.
Spike sorting is a commonly used analysis method for identifying single-units and multi-units from extracellular recordings. The extracellular recordings contain a mixture of signal components, such as neural and non-neural events, possibly due to motion and breathing artifacts or electrical interference. Identifying single and multi-unit spikes using a simple threshold-crossing method may lead to uncertainty in differentiating the actual neural spikes from non-neural spikes.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Computer Science, Faculty of Sciences and Humanities Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia.
Impedance-based biosensing has emerged as a critical technology for high-sensitivity biomolecular detection, yet traditional approaches often rely on bulky, costly impedance analyzers, limiting their portability and usability in point-of-care applications. Addressing these limitations, this paper proposes an advanced biosensing system integrating a Silicon Nanowire Field-Effect Transistor (SiNW-FET) biosensor with a high-gain amplification circuit and a 1D Convolutional Neural Network (CNN) implemented on FPGA hardware. This attempt combines SiNW-FET biosensing technology with FPGA-implemented deep learning noise reduction, creating a compact system capable of real-time viral detection with minimal computational latency.
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