ArticleBiotechnology progress
A recurrent neural network for soft sensor development using CHO stable pools in fed-batch process for SARS-CoV-2 spike protein production as a vaccine antigen.
Article in Biotechnology progress. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Fed-batch recombinant therapeutic protein (RTP) production processes utilizing Chinese Hamster Ovary (CHO) cells can take a long period of time (>10 days). Within this period, not all critical features may be measured routinely, and in fact, some are only measured once the process is terminated, complicating decision making. As a consequence, utilizing routine current day bioreactor online data to aid in next day predictions is a promising strategy for model predictive control-based feeding strategies. The article details the development of a proposed soft sensor that merges current day bioreactor online data and offline historical sampling data to generate predictions about the next day of the production process. This approach demonstrated the ability to track product titer, cell growth, key metabolites, and cumulative glucose consumption across the 17-day process with low normalized root mean squared error (nRMSE = 0.24) and low normalized mean absolute error (nMAE = 0.18) as well as high linearity with respect to ground data (average R
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Registered trials
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