Dendritic cell (DC)-based vaccines have been successfully used for immunotherapy of cancer and infections. A major obstacle is the need for high-level class A cleanroom cGMP facilities for DC generation. The CliniMACS Prodigy® (Prodigy) represents a new platform integrating all GMP-compliant manufacturing steps in a closed system for automated production of various cellular products, notably T cells, NK cells and CD34 cells. We now systematically tested its suitability for producing human mature monocyte-derived DCs (Mo-DCs), and optimized it by directly comparing the Prodigy approach to our established standard production of Mo-DCs from elutriated monocytes in dishes or bags. Upon step-by-step identification of an optimal cell concentration for the Prodigy's CentriCult culture chamber, the total yield (% of input CD14 monocytes), phenotype, and functionality of mature Mo-DCs were equivalent to those generated by the standard protocol. Technician's labor time was comparable for both methods, but the Prodigy approach significantly reduced hands-on time and high-level clean room resources. In summary, using our optimized conditions for the CliniMACS Prodigy, human Mo-DCs for clinical application can be generated almost automatically in a fully closed system. A significant drawback of the Prodigy approach was, however, that due to the limited size of the CentriCult culture chamber, in contrast to our standard semi-closed elutriation approach, only one fourth of an apheresis could be processed at once.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jim.2018.09.012DOI Listing

Publication Analysis

Top Keywords

prodigy approach
12
closed system
8
centricult culture
8
culture chamber
8
prodigy
5
automated closed-system
4
closed-system manufacturing
4
manufacturing human
4
human monocyte-derived
4
monocyte-derived dendritic
4

Similar Publications

[Émile Nelligan (1879-1941) Our Contemporary: Between Freedom and Constraint].

Sante Ment Que

December 2024

Université de Montréal, Québec, Canada; George Washington University, Washington, DC 20052, États-Unis.

Introduction This essay reviews the case of Émile Nelligan, Quebec's most celebrated poet and the Institute's most famous patient. Writing a clinical case study should be relevant for readers of a scientific medical journal. This allows the transfer of important clinical knowledge for optimal medical care of patients.

View Article and Find Full Text PDF
Article Synopsis
  • - The study compares two surgical methods for treating primary vesicoureteral reflux in pediatric patients: the traditional open Lich-Gregoir reimplantation and the newer laparoscopic approach, assessing their complications and success rates.
  • - Data were collected retrospectively from clinical charts of 68 patients who underwent either surgery between 2013 and 2017, with no notable differences found in operation times, hospital stays, or complication rates between the two groups.
  • - The success rates were 85.2% for the open group and 90.2% for the laparoscopic group, indicating no significant difference, and the lower overall success rates may be attributed to the complex cases involving high-grade reflux in both surgical methods.
View Article and Find Full Text PDF

Scalable log-ratio lasso regression for enhanced microbial feature selection with FLORAL.

Cell Rep Methods

November 2024

Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Los Angeles, CA, USA; Hematologic Malignancies Research Institute, City of Hope National Medical Center, Los Angeles, CA, USA; Comprehensive Cancer Center, City of Hope National Medical Center, Los Angeles, CA, USA. Electronic address:

Identifying predictive biomarkers of patient outcomes from high-throughput microbiome data is of high interest, while existing computational methods do not satisfactorily account for complex survival endpoints, longitudinal samples, and taxa-specific sequencing biases. We present FLORAL, an open-source tool to perform scalable log-ratio lasso regression and microbial feature selection for continuous, binary, time-to-event, and competing risk outcomes, with compatibility for longitudinal microbiome data as time-dependent covariates. The proposed method adapts the augmented Lagrangian algorithm for a zero-sum constraint optimization problem while enabling a two-stage screening process for enhanced false-positive control.

View Article and Find Full Text PDF

A deep learning approach to optimize remaining useful life prediction for Li-ion batteries.

Sci Rep

October 2024

Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea.

Accurately predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is vital for improving battery performance and safety in applications such as consumer electronics and electric vehicles. While the prediction of RUL for these batteries is a well-established field, the current research refines RUL prediction methodologies by leveraging deep learning techniques, advancing prediction accuracy. This study proposes AccuCell Prodigy, a deep learning model that integrates auto-encoders and long short-term memory (LSTM) layers to enhance RUL prediction accuracy and efficiency.

View Article and Find Full Text PDF

To meet the prodigious bioenergetic demands of the photoreceptors, glucose and other nutrients must traverse the retinal pigment epithelium (RPE), a polarised monolayer of cells that lie at the interface between the outer retina and the choroid, the principal vascular layer of the eye. Recent investigations have revealed a metabolic ecosystem in the outer retina where the photoreceptors and RPE engage in a complex exchange of sugars, amino acids, and other metabolites. Perturbation of this delicate metabolic balance has been identified in the aging retina, as well as in age-related macular degeneration (AMD), the leading cause of blindness in the Western world.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!