Hasil untuk "Manufactures"

Brian N. Turner, Robert J. Strong, S. Gold

X. Xu

K. Green, Pamela J. Zelbst, Jeramy Meacham et al.

Xinchen Wang, K. Maeda, Arne Thomas et al.

Chang-tai Hsieh, Peter J. Klenow

Zhi-Yuan Liu, Dandan Zhao, Pei Wang et al.

S. Vargo, R. Lusch

D. Teece

John Paul MacDuffie

Ronald T. Azuma

J. Covin, D. Slevin

A. Nel, T. Xia, Lutz Mädler et al.

Qinghua Zhu, Joseph Sarkis

Fourth Edition, Averill M. Law

Rachna Shah, Peter T. Ward

D. Lambert

B. Levine, J. Miskin, K. Wonnacott et al.

Immunotherapy using chimeric antigen receptor-modified T cells has demonstrated high response rates in patients with B cell malignancies, and chimeric antigen receptor T cell therapy is now being investigated in several hematologic and solid tumor types. Chimeric antigen receptor T cells are generated by removing T cells from a patient's blood and engineering the cells to express the chimeric antigen receptor, which reprograms the T cells to target tumor cells. As chimeric antigen receptor T cell therapy moves into later-phase clinical trials and becomes an option for more patients, compliance of the chimeric antigen receptor T cell manufacturing process with global regulatory requirements becomes a topic for extensive discussion. Additionally, the challenges of taking a chimeric antigen receptor T cell manufacturing process from a single institution to a large-scale multi-site manufacturing center must be addressed. We have anticipated such concerns in our experience with the CD19 chimeric antigen receptor T cell therapy CTL019. In this review, we discuss steps involved in the cell processing of the technology, including the use of an optimal vector for consistent cell processing, along with addressing the challenges of expanding chimeric antigen receptor T cell therapy to a global patient population.

Bo-Hu Li, Baocun Hou, Wentao Yu et al.

Rahul Rai, M. Tiwari, Dmitry Ivanov et al.

The machine learning (ML) field has deeply impacted the manufacturing industry in the context of the Industry 4.0 paradigm. The industry 4.0 paradigm encourages the usage of smart sensors, devices, and machines, to enable smart factories that continuously collect data pertaining to production. ML techniques enable the generation of actionable intelligence by processing the collected data to increase manufacturing efficiency without significantly changing the required resources. Additionally, the ability of ML techniques to provide predictive insights has enabled discerning complex manufacturing patterns and offers a pathway for an intelligent decision support system in a variety of manufacturing tasks such as intelligent and continuous inspection, predictive maintenance, quality improvement, process optimisation, supply chain management, and task scheduling. While different ML techniques have been used in a variety of manufacturing applications in the past, many open questions and challenges remain, from Big data curation, storage, and understanding, data reasoning to enable real-time actionable intelligence to topics such as edge computing and cybersecurity aspects of smart manufacturing. Hence, this special issue is focused on bringing together a wide range of researchers to report the latest efforts in the fundamental theoretical as well as experimental aspects of ML and their applications in manufacturing and productionsystems.

Lianhui Li, Bingbing Lei, Chunlei Mao