Innovative fixed bed bioreactor platform: Enabling linearly scalable adherent cell biomanufacturing with real-time biomass prediction from nutrient consumption.

Scalable single-use adherent cell-based biomanufacturing platforms are essential for unlocking the full potential of cell and gene therapies. The primary objective of this study is to design and develop a novel fixed bed bioreactor platform tailored specifically for scaling up adherent cell culture. The bioreactor comprises a packed bed of vertically stacked woven polyethylene terephthalate mesh discs, sandwiched between two-fluid guide plates. Leveraging computational fluid dynamics modeling, we optimized bioreactor design to achieve uniform flow with minimal shear stress. Residence time distribution measurements demonstrated excellent flow uniformity with plug flow characteristics. Periodic media sampling coupled with offline analysis revealed minimal gradients of crucial metabolites (glucose, glutamine, lactate, and ammonia) across the bioreactor during cell growth. Furthermore, the bioreactor platform demonstrated high performance in automated cell harvesting, with ≈96% efficiency and ≈98% viability. It also exhibited linear scalability in both operational parameters and performance for cell culture and adeno-associated virus vector production. We developed mathematical models based on oxygen uptake rates to accurately predict cell growth curves and estimate biomass in real-time. This study demonstrates the effectiveness of the developed fixed-bed bioreactor platform in enabling scalable adherent cell-based biomanufacturing with high productivity and process control.

Phonetic categorization relies on motor simulation, but combinatorial phonological computations are abstract.

To identify a spoken word (e.g., dog), people must categorize the speech steam onto distinct units (e.g., contrast dog/fog,) and extract their combinatorial structure (e.g., distinguish dog/god). However, the mechanisms that support these two core functions are not fully understood. Here, we explore this question using transcranial magnetic stimulation (TMS). We show that speech categorization engages the motor system, as stimulating the lip motor area has opposite effects on labial (ba/pa)- and coronal (da/ta) sounds. In contrast, the combinatorial computation of syllable structure engages Broca's area, as its stimulation disrupts sensitivity to syllable structure (compared to motor stimulation). We conclude that the two ingredients of language-categorization and combination-are distinct functions in human brains.

Enabling Precision Medicine in Cancer Care Through a Molecular Data Warehouse: The Moffitt Experience.

PURPOSE: The use of genomics within cancer research and clinical oncology practice has become commonplace. Efforts such as The Cancer Genome Atlas have characterized the cancer genome and suggested a wealth of targets for implementing precision medicine strategies for patients with cancer. The data produced from research studies and clinical care have many potential secondary uses beyond their originally intended purpose. Effective storage, query, retrieval, and visualization of these data are essential to create an infrastructure to enable new discoveries in cancer research. METHODS: Moffitt Cancer Center implemented a molecular data warehouse to complement the extensive enterprise clinical data warehouse (Health and Research Informatics). Seven different sequencing experiment types were included in the warehouse, with data from institutional research studies and clinical sequencing. RESULTS: The implementation of the molecular warehouse involved the close collaboration of many teams with different expertise and a use case-focused approach. Cornerstones of project success included project planning, open communication, institutional buy-in, piloting the implementation, implementing custom solutions to address specific problems, data quality improvement, and data governance, unique aspects of which are featured here. We describe our experience in selecting, configuring, and loading molecular data into the molecular data warehouse. Specifically, we developed solutions for heterogeneous genomic sequencing cohorts (many different platforms) and integration with our existing clinical data warehouse. CONCLUSION: The implementation was ultimately successful despite challenges encountered, many of which can be generalized to other research cancer centers.