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Record W2753939051

The Effect of Various Stabilizers on Preserving Immunogenicity of Lyophilized Mumps Vaccines.

2017· article· en· W2753939051 on OpenAlexaff
Razieh Kamali Jamil, Mohammad Shayestehour, Zohreh Azita Sadigh, Mohammad Taqavian, M K Shahkarami, Fatemeh Esna‐Ashari, Reza Shahbazi, Ashraf Mohammadi, A Foroughi, Bizhan Romani

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldMedicine
TopicVirology and Viral Diseases
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsImmunogenicityMumps virusAntibodyTrehaloseVirologyMumps vaccineInoculationHemagglutinationHemagglutination assayMedicineVaccinationVirusMicrobiologyChemistryImmunologyBiologyTiterRubellaBiochemistry
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Chemical stabilizers are added to live attenuated vaccines for enhancing the virus stability. The aim of this study was to evaluate the effect of various stabilizers on preserving immunogenicity of lyophilized mumps vaccines. STUDY DESIGN: An experimental study. METHODS: Three mumps vaccines with different formulations were inoculated to three groups of Guinea pigs. Sterile water was injected to eight Guinea pigs as a control group. Blood samples were collected before inoculation and on 14, 28 and 42 d after vaccine injection. Mumps antibodies in the sera were measured using hemagglutination inhibition assay (HAI). RESULTS: All three formulated mumps vaccines induced antibody in Guinea pigs after two weeks. Formulation 1 containing trehalose dihydrate and formulation 2 comprised human serum albumin stimulated antibodies in the higher level than Razi routine formulation. CONCLUSIONS: Various stabilizers have different preservation potencies that differently affect immune response against virus. More stable and more immunogenic vaccines can be produced using stabilizers containing trehalose dihydrate.

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)0.000
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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.017
GPT teacher head0.270
Teacher spread0.254 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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