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Record W2769305996 · doi:10.1371/journal.pone.0187048

Increasing vaccine production using pulsed ultrasound waves

2017· article· en· W2769305996 on OpenAlexafffund
Jida Xing, Shrishti Singh, Yupeng Zhao, Yan Duan, Huining Guo, Chenxia Hu, Allan Ma, Rajan George, James Z. Xing, Ankarao Kalluri, Isaac Macwan, Prabir Patra, Jie Chen

Bibliographic record

VenuePLoS ONE · 2017
Typearticle
Languageen
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsUniversity of British ColumbiaUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsUltrasoundStimulationSf9Hepatitis B virusBiophysicsMedicineVirusBiologyVirologyInternal medicineBiochemistryRecombinant DNAGene

Abstract

fetched live from OpenAlex

Vaccination is a safe and effective approach to prevent deadly diseases. To increase vaccine production, we propose that a mechanical stimulation can enhance protein production. In order to prove this hypothesis, Sf9 insect cells were used to evaluate the increase in the expression of a fusion protein from hepatitis B virus (HBV S1/S2). We discovered that the ultrasound stimulation at a frequency of 1.5 MHz, intensity of 60 mW/cm2, for a duration of 10 minutes per day increased HBV S1/S2 by 27%. We further derived a model for transport through a cell membrane under the effect of ultrasound waves, tested the key assumptions of the model through a molecular dynamics simulation package, NAMD (Nanoscale Molecular Dynamics program) and utilized CHARMM force field in a steered molecular dynamics environment. The results show that ultrasound waves can increase cell permeability, which, in turn, can enhance nutrient / waste exchange thus leading to enhanced vaccine production. This finding is very meaningful in either shortening vaccine production time, or increasing the yield of proteins for use as vaccines.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.134
GPT teacher head0.341
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2017
Admission routes2
Has abstractyes

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Same venuePLoS ONESame topicViral gastroenteritis research and epidemiologyFrench-language works237,207