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Record W3031568341 · doi:10.1021/acsnano.0c02240

Suppressing Subcapsular Sinus Macrophages Enhances Transport of Nanovaccines to Lymph Node Follicles for Robust Humoral Immunity

2020· article· en· W3031568341 on OpenAlexafffund
Yi-Nan Zhang, Wilson Poon, Elana Sefton, Warren C. W. Chan

Bibliographic record

VenueACS Nano · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Cancer Society Research InstituteCanadian Institutes of Health Research
KeywordsLymph nodeImmune systemAntibodyAntigenHumoral immunityLymphImmunityImmunologyBiologyCell biologyChemistryMedicinePathology

Abstract

fetched live from OpenAlex

Nanovaccines need to be transported to lymph node follicles to induce humoral immunity and generate neutralizing antibodies. Here, we discovered that subcapsular sinus macrophages play a barrier role to prevent nanovaccines from accessing lymph node follicles. This is illustrated by measuring the humoral immune responses after removing or functionally altering these cells in the nanovaccine transport process. We achieved up to 60 times more antigen-specific antibody production after suppressing subcapsular sinus macrophages. The degree of the enhanced antibody production is dependent on the nanovaccine dose and size, formulation, and administration time. We further found that pharmacological agents that disrupt the macrophage uptake function can be considered as adjuvants in vaccine development. Immunizing mice using nanovaccines formulated with these agents can induce more than 30 times higher antigen-specific antibody production compared to nanovaccines alone. These findings suggest that altering transport barriers to enable more of the nanovaccine to be delivered to the lymph node follicles for neutralizing antibody production is an effective strategy to boost vaccination.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.254
Teacher spread0.230 · 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 teacher head, not a consensus.

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

Citations56
Published2020
Admission routes2
Has abstractyes

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