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Record W2070350831 · doi:10.1002/bit.22365

Specific immuno capturing of the Staphylococcal superantigen toxic‐shock syndrome toxin‐1 in plasma

2009· article· en· W2070350831 on OpenAlexaff
Hendrik Adams, Walter J. Brummelhuis, Bram Maassen, Nathalie van Egmond, Mohamed El Khattabi, Frank Detmers, Pim Hermans, Branko Braam, Jord C. Stam, Theo Verrips

Bibliographic record

VenueBiotechnology and Bioengineering · 2009
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsUniversity of Alberta HospitalAlberta Hospital Edmonton
Fundersnot available
KeywordsSuperantigenToxic shock syndromeToxinMicrobiologyStaphylococcus aureusMonoclonal antibodySepsisEnterotoxinAntibodyShock (circulatory)BiologyChemistryBacteriaEscherichia coliImmunologyBiochemistryGeneMedicineGenetics

Abstract

fetched live from OpenAlex

Toxic-shock syndrome is primarily caused by the Toxic-shock syndrome toxin 1 (TSST-1), which is secreted by the Gram-positive bacterium Staphylococcus aureus. The toxin belongs to a family of superantigens (SAgs) which exhibit several shared biological properties, including the induction of massive cytokine release and V(beta)-specific T-cell proliferation. In this study we explored the possibility to use monoclonal Variable domains of Llama Heavy-chain antibodies (VHH) in the immuno capturing of TSST-1 from plasma. Data is presented that the selected VHHs are highly specific for TSST-1 and can be efficiently produced in large amounts in yeast. In view of affinity chromatography, the VHHs are easily coupled to beads, and are able to deplete TSST-1 from plasma at very low, for example, pathologically relevant, concentrations. When spiked with 4 ng/mL TSST-1 more than 96% of TSST-1 was depleted from pig plasma. These data pave the way to further explore application of high-affinity columns in the specific immuno depletion of SAgs in experimental sepsis models and in sepsis in humans.

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 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 score0.383
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.011
GPT teacher head0.231
Teacher spread0.220 · 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.

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

Citations26
Published2009
Admission routes1
Has abstractyes

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