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Record W3203648464 · doi:10.21769/bioprotoc.4175

Measuring Total Classical Pathway and Activities of Individual Components of the Mouse Complement Pathway

2021· article· en· W3203648464 on OpenAlexaff
Wioleta M. Zelek

Bibliographic record

VenueBIO-PROTOCOL · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicComplement system in diseases
Canadian institutionsInstitute of Infection and Immunity
FundersHealth and Care Research Wales
KeywordsComplement (music)Complement systemAlternative complement pathwayClassical complement pathwayBiologyInnate immune systemPhagocytosisAntibodyComplement component 5Complement component 3Cell biologyImmunologyComplement control proteinHemolysisComputational biologyImmune systemBiochemistryGene

Abstract

fetched live from OpenAlex

The complement system is a central component of innate immunity, responsible for recognition and killing of bacteria by tagging invaders through opsonisation, thereby promoting phagocytosis, and by direct lysis. Complement activity is routinely measured using functional assays that utilise erythrocytes as targets. The classical pathway haemolytic assay (CH50) with antibody sensitised sheep erythrocytes as target is used worldwide in clinical and research laboratories to measure complement activity in human and rodent sera. While there are no particular limitations in the human assay, measuring complement in mouse serum is more difficult and usually requires large amounts of serum, which is challenging to collect in experiments. In particular, it is challenging to measure the activities of individual mouse complement proteins. To overcome this hurdle, we have developed protocols that employ human sera depleted of single complement proteins as the source of the other complement proteins and test mouse serum to restore the relevant component. This simple haemolytic assay is a useful tool for confirming natural or engineered complement deficiencies and complement dysregulation in mouse models.

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 score0.093
Threshold uncertainty score0.594

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 science0.0000.001
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 head0.071
GPT teacher head0.283
Teacher spread0.212 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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