MétaCan
Menu
Back to cohort
Record W4304112069 · doi:10.1016/j.xpro.2022.101755

Protocol to desensitize human and murine mast cells after polyclonal IgE sensitization

2022· article· en· W4304112069 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSTAR Protocols · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMast cells and histamine
Canadian institutionsMcMaster University Medical Centre
FundersNational Institute of Allergy and Infectious DiseasesAgencia Estatal de InvestigaciónInstituto de Salud Carlos IIINational Institutes of HealthFaculty of Science and Engineering, University of ManchesterFundación de la Sociedad Española de Alergología e Inmunología ClínicaFederación Española de Enfermedades RarasNutricia Research Foundation
KeywordsDegranulationSensitizationCD63Immunoglobulin EPolyclonal antibodiesDesensitization (medicine)Flow cytometryImmunologyCell biologyChemistryMolecular biologyBiologyAntibodyBiochemistry

Abstract

fetched live from OpenAlex

In this protocol, we provide detailed instructions to desensitize human and murine mast cells (MCs) after polyclonal IgE sensitization. Moreover, we specify the steps for MC degranulation assessment after desensitization, measuring CD63 and CD107a expression by flow cytometry and β-hexosaminidase activity. Desensitized MCs can be used directly for co-culture with other cell types, immunofluorescence, live imaging, and omics approaches. For complete details on the use and execution of this protocol, please refer to López-Sanz et al. (2022).

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.868
Threshold uncertainty score0.993

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)0.0080.000

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 head0.013
GPT teacher head0.272
Teacher spread0.259 · 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